{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import akshare as ak\n",
    "import json\n",
    "import pandas as pd\n",
    "import sys\n",
    "import datetime\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Line\n",
    "from pyecharts.commons.utils import JsCode\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Scatter\n",
    "from pyecharts.options import InitOpts\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar, Grid, Line\n",
    "import matplotlib.pyplot as plt\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def convert_time(x):\n",
    "    return str(x).replace(' 00:00:00','')\n",
    "    \n",
    "import re\n",
    "sys.path.append('..')\n",
    "from configure.settings import DBSelector\n",
    "db = DBSelector()\n",
    "mongo = db.mongo('qq')\n",
    "fund = mongo['fund']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_net_value(code):\n",
    "    '''\n",
    "    获取基金的净值\n",
    "    '''\n",
    "    fund_open_fund_info_em_df = ak.fund_open_fund_info_em(fund=code, indicator=\"累计净值走势\")\n",
    "    return fund_open_fund_info_em_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=get_net_value('001220')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>净值日期</th>\n",
       "      <th>累计净值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>2023-02-22</td>\n",
       "      <td>1.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>2023-02-23</td>\n",
       "      <td>1.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>2023-02-24</td>\n",
       "      <td>1.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>2023-02-27</td>\n",
       "      <td>1.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>1.627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            净值日期   累计净值\n",
       "1876  2023-02-22  1.639\n",
       "1877  2023-02-23  1.633\n",
       "1878  2023-02-24  1.624\n",
       "1879  2023-02-27  1.615\n",
       "1880  2023-02-28  1.627"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dump_mongo(df,code):\n",
    "    js_str = df.to_json(orient='index')\n",
    "    js_dict = json.loads(js_str)\n",
    "    js_list = js_dict.values()\n",
    "\n",
    "    fund.drop_collection(code)\n",
    "    \n",
    "    try:\n",
    "        fund[code].insert_many(js_list)\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def start_crawl(code):\n",
    "    df = get_net_value(code)\n",
    "    df['净值日期']=df['净值日期'].astype(str)\n",
    "    dump_mongo(df,code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt='''\n",
    "1、$国泰大农业股票A(F001579)$ 基金经理程洲，今年以来回报-4.04%，近一年回报0.22%，近一年最大回撤-14.40%，二季度末规模12.87亿。\n",
    "\n",
    "2、$银华农业产业股票(F005106)$ 基金经理唐能，今年以来回报-6.90%，近一年回报1.01%，近一年最大回撤-22.40%，二季度末规模12.78亿。\n",
    "\n",
    "3、$前海开源沪港深农业混合（LOF）A(F164403)$ 基金经理吴国清,刘宏，今年以来回报2.97%，近一年回报8.20%，近一年最大回撤-21.68%，二季度末规模4.21亿\n",
    "\n",
    "4、$嘉实农业产业股票(F003634)$ 基金经理吴越,朱子君，今年以来回报-7.38%，近一年回报3.37%，近一年最大回撤-19.06%，二季度末规模26.71亿。\n",
    "\n",
    "5、$财通智慧成长混合A(F009062)$ 基金经理金梓才、钟俊，今年以来回报2.89%，近一年回报-21.17%，近一年最大回撤-30.49%，二季度末规模2.56亿。\n",
    "\n",
    "6、$天弘中证农业主题指数A(F010769)$ 跟踪中证农业主题指数，今年以来回报-4.32%，近一年回报2.76%，近一年最大回撤-16.96%，二季度末规模2.24亿。\n",
    "\n",
    "7、$国泰中证畜牧养殖ETF联接A(F012724)$ 跟踪中证畜牧养殖指数，今年以来回报-2.47%，近一年回报14.62%，近一年最大回撤-20.36%，二季度末规模2.38亿。\n",
    "\n",
    "8、$华宝中证全指农牧渔指数发起式A(F013471)$ 跟踪中证全指农牧渔指数，今年以来回报-2.81%，成立以来回报1.33%，近一年最大回撤-17.85%，二季度末规模0.21亿。\n",
    "\n",
    "9、$前海开源中证大农业指数增强A(F001027)$ 跟踪中证大农业指数，今年以来回报-10.06%，近一年回报-6.79%，近一年最大回撤-19.06%，二季度末规模6.82亿。（以上数据截至于2022年9月15日）\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt='[$南方转型混合A(F001667)$](http://xueqiu.com/S/F001667) [$易方达瑞恒混合(F001832)$](http://xueqiu.com/S/F001832) [$易方达新经济混合(F001018)$](http://xueqiu.com/S/F001018) [$华安安信消费混合(F519002)$](http://xueqiu.com/S/F519002) [$万家颐和灵活配置混合(F519198)$](http://xueqiu.com/S/F519198) [$国富深化价值混合(F450004)$](http://xueqiu.com/S/F450004) [$工银战略转型股票(F000991)$](http://xueqiu.com/S/F000991) [$招商制造业混合(F001869)$](http://xueqiu.com/S/F001869) [$易方达行业领先企业(F110015)$](http://xueqiu.com/S/F110015) [$民生加银研究精选混合(F001220)$](http://xueqiu.com/S/F001220) '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_txt = '''\n",
    "118002\t易方达标普消费品\n",
    "486002\t工银全球精选股票\n",
    "006373\t国富全球科技互联\n",
    "000369\t广发全球医疗保健\n",
    "100055\t富国全球科技互联\n",
    "160416\t华安标普全球石油\n",
    "320013\t诺安全球黄金\n",
    "161815\t银华抗通胀主题\n",
    "'''\n",
    "re_pattern = re.findall('(\\d+)\\s+(\\w+)',code_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_list =[]\n",
    "fund_dict ={}\n",
    "for code,name in re_pattern:\n",
    "    code_list.append(code)\n",
    "    fund_dict[code]=name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_list = re.findall('\\(F(\\d+)\\)',txt)\n",
    "fund_name = re.findall('\\$(.*?)\\$',txt)\n",
    "fund_dict = dict(zip(code_list,fund_name))\n",
    "name_list  = list(fund_dict.values())\n",
    "simple_name = [i.split('(')[0] for i in name_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(code_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['001667',\n",
       " '001832',\n",
       " '001018',\n",
       " '519002',\n",
       " '519198',\n",
       " '450004',\n",
       " '000991',\n",
       " '001869',\n",
       " '110015',\n",
       " '001220']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for code in code_list:\n",
    "    start_crawl(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'001667': '南方转型混合A(F001667)',\n",
       " '001832': '易方达瑞恒混合(F001832)',\n",
       " '001018': '易方达新经济混合(F001018)',\n",
       " '519002': '华安安信消费混合(F519002)',\n",
       " '519198': '万家颐和灵活配置混合(F519198)',\n",
       " '450004': '国富深化价值混合(F450004)',\n",
       " '000991': '工银战略转型股票(F000991)',\n",
       " '001869': '招商制造业混合(F001869)',\n",
       " '110015': '易方达行业领先企业(F110015)',\n",
       " '001220': '民生加银研究精选混合(F001220)'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# after fetch data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_profit_line(df,code):\n",
    "    title=\"{}基金收益率曲线\".format(code)\n",
    "    X=df['净值日期'].tolist()\n",
    "    Y=list(map(lambda x:round(x,2),df['累计净值'].tolist()))\n",
    "    c = (\n",
    "        Line()\n",
    "        .add_xaxis(X)\n",
    "        .add_yaxis('', Y, is_smooth=True,\n",
    "            label_opts=opts.LabelOpts(is_show=False),\n",
    "            linestyle_opts=opts.LineStyleOpts(width=1,color='rgb(255, 0, 0)'),\n",
    "        ).set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title=title),\n",
    "            xaxis_opts=opts.AxisOpts(\n",
    "                                    name='日期',\n",
    "                                    # min_interval=5,\n",
    "                                    splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "                                            axislabel_opts=opts.LabelOpts(rotate=45),\n",
    "\n",
    "                                    ),\n",
    "            yaxis_opts=opts.AxisOpts(\n",
    "                                    min_=min(Y),\n",
    "                                    max_=max(Y),\n",
    "                splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "            )\n",
    "                                        ).set_colors(['green'])\n",
    "        .render(f\"../plot_image/{title}.html\")\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for code,name in fund_dict.items():\n",
    "    result = []\n",
    "    for item in fund[code].find({},{'_id':0}):\n",
    "        result.append(item)\n",
    "    df = pd.DataFrame(result)\n",
    "    plot_profit_line(df,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "W=10000\n",
    "focus_num = [\n",
    "3.98*W,\n",
    "7519,\n",
    "72,\n",
    "309,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = (\n",
    "    Scatter(InitOpts(width='800px',height='500px'))\n",
    "    .add_xaxis(simple_name)\n",
    "    .add_yaxis('A',focus_num,label_opts=opts.LabelOpts(is_show=True))\n",
    "    .set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "                                    # name='日期',\n",
    "                                    # min_interval=5,\n",
    "                                    splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "                                            axislabel_opts=opts.LabelOpts(rotate=-45),\n",
    "\n",
    "                                    ),\n",
    "        yaxis_opts=opts.AxisOpts(    \n",
    "            min_=0,\n",
    "                splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "            ),\n",
    "        title_opts=opts.TitleOpts(title=\"welcome\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(type_=\"size\",max_=150000, min_=2000),\n",
    "    )\n",
    "    .render(\"5fund.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = (\n",
    "    Grid(init_opts=opts.InitOpts())\n",
    "    .add(c, grid_opts=opts.GridOpts(pos_bottom=\"40%\"))\n",
    "    .render(\"grid_verticalfund.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "c = (\n",
    "    Bar()\n",
    "    .add_xaxis(\n",
    "    new_name\n",
    "    )\n",
    "    .add_yaxis(\"基金关注度\", focus_num,label_opts=opts.LabelOpts(is_show=True))\n",
    "    .set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=0)),\n",
    "        title_opts=opts.TitleOpts(title=\"基金关注度\"),\n",
    "    )\n",
    "    # .render(\"基金关注度.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_name = []\n",
    "for name in simple_name:\n",
    "    new_name.append('\\n'.join(list(name)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['华\\n安\\n纳\\n斯\\n达\\n克\\n1\\n0\\n0\\n指\\n数',\n",
       " '博\\n时\\n标\\n普\\n5\\n0\\n0\\nE\\nT\\nF\\n联\\n接',\n",
       " '天\\n弘\\n恒\\n生\\n科\\n技\\n指\\n数\\nA',\n",
       " '嘉\\n实\\n港\\n股\\n通\\n新\\n经\\n济\\n指\\n数\\nA',\n",
       " '华\\n夏\\n恒\\n生\\nE\\nT\\nF\\n联\\n接',\n",
       " '易\\n方\\n达\\n中\\n概\\n互\\n联\\n5\\n0\\nE\\nT\\nF\\n联\\n接\\n人\\n民\\n币\\nA',\n",
       " '交\\n银\\n中\\n证\\n海\\n外\\n中\\n国\\n互\\n联\\n网\\n指\\n数',\n",
       " '华\\n安\\n香\\n港\\n精\\n选',\n",
       " '中\\n欧\\n丰\\n泓\\n沪\\n港\\n深\\nA']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_net_value_df(code):\n",
    "    '''\n",
    "    从mongodb获取数据\n",
    "    '''\n",
    "    result=[]\n",
    "    for item in fund[code].find({},{'_id':0}):\n",
    "        result.append(item)\n",
    "    df = pd.DataFrame(result)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_year(start,end):\n",
    "    '''\n",
    "    获取年份\n",
    "    '''\n",
    "    year = (datetime.datetime.strptime(end,'%Y-%m-%d')- datetime.datetime.strptime(start,'%Y-%m-%d')).days/365\n",
    "    return year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_profit_rate(df):\n",
    "    '''\n",
    "    计算所有的收益率\n",
    "    '''\n",
    "    return round((df['累计净值'].iloc[-1]-df['累计净值'].iloc[0])/df['累计净值'].iloc[0],4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_yearly_profit_rate(df):\n",
    "    '''\n",
    "    计算年化收益率\n",
    "    '''\n",
    "    \n",
    "    start = df['净值日期'].iloc[0]\n",
    "    end = df['净值日期'].iloc[-1]\n",
    "    year = get_year(start,end)\n",
    "    print('开始时间：',start)\n",
    "    print('成立年数：',round(year,2),'年')\n",
    "    profit = get_profit_rate(df)\n",
    "    print('成立以来累积收益率:',profit)\n",
    "    year_profit = (1+profit)**(1/year)-1\n",
    "    return start,round(year,4),profit,round(year_profit,4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def fund_profit(code):\n",
    "    '''\n",
    "    生成字典\n",
    "    '''\n",
    "\n",
    "    df = get_net_value_df(code)\n",
    "    max_withdraw,max_date_index = get_max_withdraw(df['累计净值'].tolist())\n",
    "    start,year,profit,year_profit = get_yearly_profit_rate(df)\n",
    "    d={}\n",
    "    d['代码']=code\n",
    "    d['名称']=fund_dict.get(code)\n",
    "    d['发行日期']=start\n",
    "    d['成立年数']=year\n",
    "    d['累积收益率']=profit\n",
    "    d['年化收益率']=year_profit\n",
    "    d['最大回撤']=max_withdraw\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_max_withdraw(indexs):\n",
    "    max_withdraw = 0\n",
    "    start_date_index =0\n",
    "    max_date_index =0\n",
    "    last_high = indexs[0]\n",
    "    \n",
    "    for index,current in enumerate(indexs):\n",
    "        # 遍历所有数据\n",
    "        if current>last_high:\n",
    "            last_high=current\n",
    "            # start_date_index=index\n",
    "            continue\n",
    "\n",
    "        if (last_high-current)/last_high>max_withdraw:\n",
    "            # 找到一个最大值时，保存其位置\n",
    "            max_withdraw = (last_high-current)/last_high\n",
    "            max_date_index=index\n",
    "\n",
    "    return max_withdraw,max_date_index # 变成百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始时间： 2016-08-17\n",
      "成立年数： 6.54 年\n",
      "成立以来累积收益率: 1.045\n",
      "开始时间： 2018-01-10\n",
      "成立年数： 5.14 年\n",
      "成立以来累积收益率: 1.651\n",
      "开始时间： 2015-02-12\n",
      "成立年数： 8.05 年\n",
      "成立以来累积收益率: 3.065\n",
      "开始时间： 2013-06-24\n",
      "成立年数： 9.69 年\n",
      "成立以来累积收益率: 3.8348\n",
      "开始时间： 2016-06-23\n",
      "成立年数： 6.69 年\n",
      "成立以来累积收益率: 1.2263\n",
      "开始时间： 2008-07-03\n",
      "成立年数： 14.67 年\n",
      "成立以来累积收益率: 2.1763\n",
      "开始时间： 2015-02-16\n",
      "成立年数： 8.04 年\n",
      "成立以来累积收益率: 2.926\n",
      "开始时间： 2015-12-02\n",
      "成立年数： 7.25 年\n",
      "成立以来累积收益率: 1.551\n",
      "开始时间： 2009-03-26\n",
      "成立年数： 13.94 年\n",
      "成立以来累积收益率: 3.652\n",
      "开始时间： 2015-05-27\n",
      "成立年数： 7.76 年\n",
      "成立以来累积收益率: 0.627\n"
     ]
    }
   ],
   "source": [
    "result = []\n",
    "for code in code_list:\n",
    "    d = fund_profit(code)\n",
    "    result.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>名称</th>\n",
       "      <th>发行日期</th>\n",
       "      <th>成立年数</th>\n",
       "      <th>累积收益率</th>\n",
       "      <th>年化收益率</th>\n",
       "      <th>最大回撤</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>001667</td>\n",
       "      <td>南方转型混合A(F001667)</td>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>6.5370</td>\n",
       "      <td>1.0450</td>\n",
       "      <td>0.1157</td>\n",
       "      <td>0.379942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001832</td>\n",
       "      <td>易方达瑞恒混合(F001832)</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>5.1370</td>\n",
       "      <td>1.6510</td>\n",
       "      <td>0.2090</td>\n",
       "      <td>0.318008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001018</td>\n",
       "      <td>易方达新经济混合(F001018)</td>\n",
       "      <td>2015-02-12</td>\n",
       "      <td>8.0493</td>\n",
       "      <td>3.0650</td>\n",
       "      <td>0.1903</td>\n",
       "      <td>0.530127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>519002</td>\n",
       "      <td>华安安信消费混合(F519002)</td>\n",
       "      <td>2013-06-24</td>\n",
       "      <td>9.6877</td>\n",
       "      <td>3.8348</td>\n",
       "      <td>0.1766</td>\n",
       "      <td>0.553866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>519198</td>\n",
       "      <td>万家颐和灵活配置混合(F519198)</td>\n",
       "      <td>2016-06-23</td>\n",
       "      <td>6.6877</td>\n",
       "      <td>1.2263</td>\n",
       "      <td>0.1271</td>\n",
       "      <td>0.215814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>450004</td>\n",
       "      <td>国富深化价值混合(F450004)</td>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>14.6658</td>\n",
       "      <td>2.1763</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>0.563310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>000991</td>\n",
       "      <td>工银战略转型股票(F000991)</td>\n",
       "      <td>2015-02-16</td>\n",
       "      <td>8.0384</td>\n",
       "      <td>2.9260</td>\n",
       "      <td>0.1855</td>\n",
       "      <td>0.525873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>001869</td>\n",
       "      <td>招商制造业混合(F001869)</td>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>7.2466</td>\n",
       "      <td>1.5510</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>0.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>110015</td>\n",
       "      <td>易方达行业领先企业(F110015)</td>\n",
       "      <td>2009-03-26</td>\n",
       "      <td>13.9370</td>\n",
       "      <td>3.6520</td>\n",
       "      <td>0.1166</td>\n",
       "      <td>0.512736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>001220</td>\n",
       "      <td>民生加银研究精选混合(F001220)</td>\n",
       "      <td>2015-05-27</td>\n",
       "      <td>7.7644</td>\n",
       "      <td>0.6270</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.401180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       代码                   名称        发行日期     成立年数   累积收益率   年化收益率      最大回撤\n",
       "0  001667     南方转型混合A(F001667)  2016-08-17   6.5370  1.0450  0.1157  0.379942\n",
       "1  001832     易方达瑞恒混合(F001832)  2018-01-10   5.1370  1.6510  0.2090  0.318008\n",
       "2  001018    易方达新经济混合(F001018)  2015-02-12   8.0493  3.0650  0.1903  0.530127\n",
       "3  519002    华安安信消费混合(F519002)  2013-06-24   9.6877  3.8348  0.1766  0.553866\n",
       "4  519198  万家颐和灵活配置混合(F519198)  2016-06-23   6.6877  1.2263  0.1271  0.215814\n",
       "5  450004    国富深化价值混合(F450004)  2008-07-03  14.6658  2.1763  0.0820  0.563310\n",
       "6  000991    工银战略转型股票(F000991)  2015-02-16   8.0384  2.9260  0.1855  0.525873\n",
       "7  001869     招商制造业混合(F001869)  2015-12-02   7.2466  1.5510  0.1380  0.280000\n",
       "8  110015   易方达行业领先企业(F110015)  2009-03-26  13.9370  3.6520  0.1166  0.512736\n",
       "9  001220  民生加银研究精选混合(F001220)  2015-05-27   7.7644  0.6270  0.0647  0.401180"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['累积收益率']=df['累积收益率'].map(lambda x:x*100)\n",
    "df['年化收益率']=df['年化收益率'].map(lambda x:x*100)\n",
    "df['最大回撤']=df['最大回撤'].map(lambda x:x*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values('年化收益率',ascending=False).head(10).to_excel('大盘.xlsx',encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "result = pd.DataFrame()\n",
    "\n",
    "for code in code_list:\n",
    "    df_ = get_net_value_df(code)\n",
    "    df_=df_.rename(columns={'累计净值':code})\n",
    "    # del df_['日增长率']\n",
    "    df_['净值日期']=pd.to_datetime(df_['净值日期'],format='%Y-%m-%d')\n",
    "    df_=df_.set_index('净值日期',drop=True)\n",
    "    # result.append(df)\n",
    "    if len(result)==0:\n",
    "        result=df_\n",
    "\n",
    "    else:\n",
    "        result = pd.merge(result,df_,how='outer',left_index=True,right_index=True)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_info = result.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_date = list(set(index_info))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_list = []\n",
    "for i in index_date:\n",
    "    date_list.append(i.strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_date_list = list(sorted(date_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2010-05-25'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_date_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_result = result['2020-07-01':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>001667</th>\n",
       "      <th>001832</th>\n",
       "      <th>001018</th>\n",
       "      <th>519002</th>\n",
       "      <th>519198</th>\n",
       "      <th>450004</th>\n",
       "      <th>000991</th>\n",
       "      <th>001869</th>\n",
       "      <th>110015</th>\n",
       "      <th>001220</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净值日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-11</th>\n",
       "      <td>0.840</td>\n",
       "      <td>0.982</td>\n",
       "      <td>1.666</td>\n",
       "      <td>1.759</td>\n",
       "      <td>1.0306</td>\n",
       "      <td>1.6860</td>\n",
       "      <td>1.288</td>\n",
       "      <td>1.305</td>\n",
       "      <td>2.617</td>\n",
       "      <td>0.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-14</th>\n",
       "      <td>0.841</td>\n",
       "      <td>1.001</td>\n",
       "      <td>1.675</td>\n",
       "      <td>1.761</td>\n",
       "      <td>1.0304</td>\n",
       "      <td>1.6910</td>\n",
       "      <td>1.281</td>\n",
       "      <td>1.305</td>\n",
       "      <td>2.644</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-15</th>\n",
       "      <td>0.848</td>\n",
       "      <td>1.007</td>\n",
       "      <td>1.702</td>\n",
       "      <td>1.768</td>\n",
       "      <td>1.0302</td>\n",
       "      <td>1.7020</td>\n",
       "      <td>1.297</td>\n",
       "      <td>1.318</td>\n",
       "      <td>2.661</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-16</th>\n",
       "      <td>0.848</td>\n",
       "      <td>1.008</td>\n",
       "      <td>1.686</td>\n",
       "      <td>1.759</td>\n",
       "      <td>1.0290</td>\n",
       "      <td>1.6970</td>\n",
       "      <td>1.293</td>\n",
       "      <td>1.315</td>\n",
       "      <td>2.650</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-17</th>\n",
       "      <td>0.842</td>\n",
       "      <td>0.985</td>\n",
       "      <td>1.664</td>\n",
       "      <td>1.748</td>\n",
       "      <td>1.0284</td>\n",
       "      <td>1.6850</td>\n",
       "      <td>1.282</td>\n",
       "      <td>1.305</td>\n",
       "      <td>2.620</td>\n",
       "      <td>0.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-22</th>\n",
       "      <td>2.059</td>\n",
       "      <td>2.672</td>\n",
       "      <td>4.061</td>\n",
       "      <td>4.932</td>\n",
       "      <td>2.2183</td>\n",
       "      <td>3.1972</td>\n",
       "      <td>3.951</td>\n",
       "      <td>2.567</td>\n",
       "      <td>4.670</td>\n",
       "      <td>1.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-23</th>\n",
       "      <td>2.061</td>\n",
       "      <td>2.674</td>\n",
       "      <td>4.058</td>\n",
       "      <td>4.918</td>\n",
       "      <td>2.2231</td>\n",
       "      <td>3.1978</td>\n",
       "      <td>3.941</td>\n",
       "      <td>2.574</td>\n",
       "      <td>4.685</td>\n",
       "      <td>1.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-24</th>\n",
       "      <td>2.045</td>\n",
       "      <td>2.644</td>\n",
       "      <td>4.042</td>\n",
       "      <td>4.881</td>\n",
       "      <td>2.2124</td>\n",
       "      <td>3.1776</td>\n",
       "      <td>3.912</td>\n",
       "      <td>2.553</td>\n",
       "      <td>4.632</td>\n",
       "      <td>1.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-27</th>\n",
       "      <td>2.038</td>\n",
       "      <td>2.656</td>\n",
       "      <td>4.047</td>\n",
       "      <td>4.885</td>\n",
       "      <td>2.2076</td>\n",
       "      <td>3.1721</td>\n",
       "      <td>3.912</td>\n",
       "      <td>2.539</td>\n",
       "      <td>4.646</td>\n",
       "      <td>1.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-28</th>\n",
       "      <td>2.045</td>\n",
       "      <td>2.651</td>\n",
       "      <td>4.065</td>\n",
       "      <td>4.917</td>\n",
       "      <td>2.2263</td>\n",
       "      <td>3.1763</td>\n",
       "      <td>3.926</td>\n",
       "      <td>2.551</td>\n",
       "      <td>4.652</td>\n",
       "      <td>1.627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1172 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            001667  001832  001018  519002  519198  450004  000991  001869  \\\n",
       "净值日期                                                                         \n",
       "2018-05-11   0.840   0.982   1.666   1.759  1.0306  1.6860   1.288   1.305   \n",
       "2018-05-14   0.841   1.001   1.675   1.761  1.0304  1.6910   1.281   1.305   \n",
       "2018-05-15   0.848   1.007   1.702   1.768  1.0302  1.7020   1.297   1.318   \n",
       "2018-05-16   0.848   1.008   1.686   1.759  1.0290  1.6970   1.293   1.315   \n",
       "2018-05-17   0.842   0.985   1.664   1.748  1.0284  1.6850   1.282   1.305   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2023-02-22   2.059   2.672   4.061   4.932  2.2183  3.1972   3.951   2.567   \n",
       "2023-02-23   2.061   2.674   4.058   4.918  2.2231  3.1978   3.941   2.574   \n",
       "2023-02-24   2.045   2.644   4.042   4.881  2.2124  3.1776   3.912   2.553   \n",
       "2023-02-27   2.038   2.656   4.047   4.885  2.2076  3.1721   3.912   2.539   \n",
       "2023-02-28   2.045   2.651   4.065   4.917  2.2263  3.1763   3.926   2.551   \n",
       "\n",
       "            110015  001220  \n",
       "净值日期                        \n",
       "2018-05-11   2.617   0.782  \n",
       "2018-05-14   2.644   0.787  \n",
       "2018-05-15   2.661   0.790  \n",
       "2018-05-16   2.650   0.789  \n",
       "2018-05-17   2.620   0.784  \n",
       "...            ...     ...  \n",
       "2023-02-22   4.670   1.639  \n",
       "2023-02-23   4.685   1.633  \n",
       "2023-02-24   4.632   1.624  \n",
       "2023-02-27   4.646   1.615  \n",
       "2023-02-28   4.652   1.627  \n",
       "\n",
       "[1172 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_result1=recent_result*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>001667</th>\n",
       "      <th>001832</th>\n",
       "      <th>001018</th>\n",
       "      <th>519002</th>\n",
       "      <th>519198</th>\n",
       "      <th>450004</th>\n",
       "      <th>000991</th>\n",
       "      <th>001869</th>\n",
       "      <th>110015</th>\n",
       "      <th>001220</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>001667</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001832</th>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001018</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519002</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519198</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450004</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000991</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001869</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110015</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001220</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        001667  001832  001018  519002  519198  450004  000991  001869  \\\n",
       "001667    1.00    0.98    0.99    0.99    0.93    1.00    0.99    0.99   \n",
       "001832    0.98    1.00    0.96    0.95    0.92    0.97    0.97    0.97   \n",
       "001018    0.99    0.96    1.00    0.99    0.94    0.99    0.98    0.98   \n",
       "519002    0.99    0.95    0.99    1.00    0.94    0.98    0.99    0.97   \n",
       "519198    0.93    0.92    0.94    0.94    1.00    0.91    0.96    0.88   \n",
       "450004    1.00    0.97    0.99    0.98    0.91    1.00    0.98    0.99   \n",
       "000991    0.99    0.97    0.98    0.99    0.96    0.98    1.00    0.96   \n",
       "001869    0.99    0.97    0.98    0.97    0.88    0.99    0.96    1.00   \n",
       "110015    0.98    0.98    0.96    0.95    0.86    0.98    0.94    0.99   \n",
       "001220    0.99    0.96    0.99    0.98    0.91    0.99    0.97    0.99   \n",
       "\n",
       "        110015  001220  \n",
       "001667    0.98    0.99  \n",
       "001832    0.98    0.96  \n",
       "001018    0.96    0.99  \n",
       "519002    0.95    0.98  \n",
       "519198    0.86    0.91  \n",
       "450004    0.98    0.99  \n",
       "000991    0.94    0.97  \n",
       "001869    0.99    0.99  \n",
       "110015    1.00    0.97  \n",
       "001220    0.97    1.00  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.round(recent_result1.corr(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.round(recent_result.corr(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6),dpi=100)\n",
    "sns.heatmap(X,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_na = result.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_na= result_na['2020-07-01':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>118002</th>\n",
       "      <th>486002</th>\n",
       "      <th>006373</th>\n",
       "      <th>000369</th>\n",
       "      <th>100055</th>\n",
       "      <th>160416</th>\n",
       "      <th>320013</th>\n",
       "      <th>161815</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净值日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-02</th>\n",
       "      <td>2.066</td>\n",
       "      <td>2.678</td>\n",
       "      <td>1.8035</td>\n",
       "      <td>1.846</td>\n",
       "      <td>2.1676</td>\n",
       "      <td>0.876</td>\n",
       "      <td>1.195</td>\n",
       "      <td>0.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>2.115</td>\n",
       "      <td>2.726</td>\n",
       "      <td>1.8522</td>\n",
       "      <td>1.864</td>\n",
       "      <td>2.2287</td>\n",
       "      <td>0.879</td>\n",
       "      <td>1.201</td>\n",
       "      <td>0.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>2.098</td>\n",
       "      <td>2.690</td>\n",
       "      <td>1.8169</td>\n",
       "      <td>1.840</td>\n",
       "      <td>2.1858</td>\n",
       "      <td>0.860</td>\n",
       "      <td>1.202</td>\n",
       "      <td>0.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>2.093</td>\n",
       "      <td>2.714</td>\n",
       "      <td>1.8567</td>\n",
       "      <td>1.835</td>\n",
       "      <td>2.2511</td>\n",
       "      <td>0.856</td>\n",
       "      <td>1.210</td>\n",
       "      <td>0.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>2.099</td>\n",
       "      <td>2.712</td>\n",
       "      <td>1.9092</td>\n",
       "      <td>1.821</td>\n",
       "      <td>2.3073</td>\n",
       "      <td>0.833</td>\n",
       "      <td>1.202</td>\n",
       "      <td>0.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-27</th>\n",
       "      <td>3.091</td>\n",
       "      <td>3.017</td>\n",
       "      <td>2.4052</td>\n",
       "      <td>2.319</td>\n",
       "      <td>2.0626</td>\n",
       "      <td>1.527</td>\n",
       "      <td>1.242</td>\n",
       "      <td>0.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-28</th>\n",
       "      <td>3.122</td>\n",
       "      <td>3.022</td>\n",
       "      <td>2.4019</td>\n",
       "      <td>2.317</td>\n",
       "      <td>2.0475</td>\n",
       "      <td>1.536</td>\n",
       "      <td>1.237</td>\n",
       "      <td>0.671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29</th>\n",
       "      <td>3.143</td>\n",
       "      <td>3.028</td>\n",
       "      <td>2.4048</td>\n",
       "      <td>2.330</td>\n",
       "      <td>2.0381</td>\n",
       "      <td>1.549</td>\n",
       "      <td>1.238</td>\n",
       "      <td>0.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>3.174</td>\n",
       "      <td>3.062</td>\n",
       "      <td>2.4401</td>\n",
       "      <td>2.352</td>\n",
       "      <td>2.0654</td>\n",
       "      <td>1.557</td>\n",
       "      <td>1.243</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-03</th>\n",
       "      <td>3.166</td>\n",
       "      <td>3.057</td>\n",
       "      <td>2.4462</td>\n",
       "      <td>2.328</td>\n",
       "      <td>2.0851</td>\n",
       "      <td>1.566</td>\n",
       "      <td>1.247</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            118002  486002  006373  000369  100055  160416  320013  161815\n",
       "净值日期                                                                      \n",
       "2020-07-02   2.066   2.678  1.8035   1.846  2.1676   0.876   1.195   0.419\n",
       "2020-07-06   2.115   2.726  1.8522   1.864  2.2287   0.879   1.201   0.423\n",
       "2020-07-07   2.098   2.690  1.8169   1.840  2.1858   0.860   1.202   0.420\n",
       "2020-07-08   2.093   2.714  1.8567   1.835  2.2511   0.856   1.210   0.423\n",
       "2020-07-09   2.099   2.712  1.9092   1.821  2.3073   0.833   1.202   0.418\n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...\n",
       "2023-06-27   3.091   3.017  2.4052   2.319  2.0626   1.527   1.242   0.674\n",
       "2023-06-28   3.122   3.022  2.4019   2.317  2.0475   1.536   1.237   0.671\n",
       "2023-06-29   3.143   3.028  2.4048   2.330  2.0381   1.549   1.238   0.673\n",
       "2023-06-30   3.174   3.062  2.4401   2.352  2.0654   1.557   1.243   0.678\n",
       "2023-07-03   3.166   3.057  2.4462   2.328  2.0851   1.566   1.247   0.678\n",
       "\n",
       "[687 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_df = pd.DataFrame(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_df['最大回撤']=profit_df['最大回撤'].map(lambda x:round(x*100,2))\n",
    "profit_df['累积收益率']=profit_df['累积收益率'].map(lambda x:round(x*100,2))\n",
    "profit_df['年化收益率']=profit_df['年化收益率'].map(lambda x:round(x*100,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>名称</th>\n",
       "      <th>发行日期</th>\n",
       "      <th>成立年数</th>\n",
       "      <th>累积收益率</th>\n",
       "      <th>年化收益率</th>\n",
       "      <th>最大回撤</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>501059</td>\n",
       "      <td>西部利得国企红利指数增强A(F501059)</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>4.18</td>\n",
       "      <td>115.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>005561</td>\n",
       "      <td>创金合信中证红利低波动指数A(F005561)</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>4.39</td>\n",
       "      <td>65.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161907</td>\n",
       "      <td>万家中证红利指数(LOF)A(F161907)</td>\n",
       "      <td>2011-03-17</td>\n",
       "      <td>11.50</td>\n",
       "      <td>136.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>501029</td>\n",
       "      <td>华宝红利基金(F501029)</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>5.66</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>008928</td>\n",
       "      <td>泰达消费红利指数A(F008928)</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2.47</td>\n",
       "      <td>76.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>004814</td>\n",
       "      <td>中欧红利优享混合A(F004814)</td>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>4.41</td>\n",
       "      <td>69.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>005618</td>\n",
       "      <td>融通红利机会混合A(F005618)</td>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>4.47</td>\n",
       "      <td>98.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>080005</td>\n",
       "      <td>长盛量化红利混合(F080005)</td>\n",
       "      <td>2009-11-25</td>\n",
       "      <td>12.81</td>\n",
       "      <td>238.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>53.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>008163</td>\n",
       "      <td>南方大盘红利低波50ETF联接A(F008163)</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2.65</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>006658</td>\n",
       "      <td>财通中证香港红利等权指数A(F006658)</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>29.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>007751</td>\n",
       "      <td>景顺长城中证沪港深红利低波A(F007751)</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>3.02</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        代码                         名称        发行日期   成立年数  累积收益率  年化收益率   最大回撤\n",
       "0   501059     西部利得国企红利指数增强A(F501059)  2018-07-11   4.18  115.0   20.0  19.28\n",
       "1   005561    创金合信中证红利低波动指数A(F005561)  2018-04-26   4.39   65.0   12.0  19.74\n",
       "2   161907    万家中证红利指数(LOF)A(F161907)  2011-03-17  11.50  136.0    8.0  46.84\n",
       "3   501029            华宝红利基金(F501029)  2017-01-18   5.66   40.0    6.0  28.74\n",
       "4   008928         泰达消费红利指数A(F008928)  2020-03-26   2.47   76.0   26.0  18.13\n",
       "5   004814         中欧红利优享混合A(F004814)  2018-04-19   4.41   69.0   13.0  25.26\n",
       "6   005618         融通红利机会混合A(F005618)  2018-03-27   4.47   98.0   17.0  15.30\n",
       "7   080005          长盛量化红利混合(F080005)  2009-11-25  12.81  238.0   10.0  53.00\n",
       "8   008163  南方大盘红利低波50ETF联接A(F008163)  2020-01-21   2.65   23.0    8.0  12.26\n",
       "9   006658     财通中证香港红利等权指数A(F006658)  2019-04-26   3.39  -21.0   -7.0  29.02\n",
       "10  007751    景顺长城中证沪港深红利低波A(F007751)  2019-09-06   3.02   14.0    4.0  19.54"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>名称</th>\n",
       "      <th>发行日期</th>\n",
       "      <th>成立年数</th>\n",
       "      <th>累积收益率</th>\n",
       "      <th>年化收益率</th>\n",
       "      <th>最大回撤</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>008928</td>\n",
       "      <td>泰达消费红利指数A(F008928)</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>2.47</td>\n",
       "      <td>76.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>501059</td>\n",
       "      <td>西部利得国企红利指数增强A(F501059)</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>4.18</td>\n",
       "      <td>115.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>005618</td>\n",
       "      <td>融通红利机会混合A(F005618)</td>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>4.47</td>\n",
       "      <td>98.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>004814</td>\n",
       "      <td>中欧红利优享混合A(F004814)</td>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>4.41</td>\n",
       "      <td>69.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>005561</td>\n",
       "      <td>创金合信中证红利低波动指数A(F005561)</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>4.39</td>\n",
       "      <td>65.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>080005</td>\n",
       "      <td>长盛量化红利混合(F080005)</td>\n",
       "      <td>2009-11-25</td>\n",
       "      <td>12.81</td>\n",
       "      <td>238.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>53.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161907</td>\n",
       "      <td>万家中证红利指数(LOF)A(F161907)</td>\n",
       "      <td>2011-03-17</td>\n",
       "      <td>11.50</td>\n",
       "      <td>136.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>008163</td>\n",
       "      <td>南方大盘红利低波50ETF联接A(F008163)</td>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>2.65</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>501029</td>\n",
       "      <td>华宝红利基金(F501029)</td>\n",
       "      <td>2017-01-18</td>\n",
       "      <td>5.66</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>007751</td>\n",
       "      <td>景顺长城中证沪港深红利低波A(F007751)</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>3.02</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>006658</td>\n",
       "      <td>财通中证香港红利等权指数A(F006658)</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>29.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        代码                         名称        发行日期   成立年数  累积收益率  年化收益率   最大回撤\n",
       "4   008928         泰达消费红利指数A(F008928)  2020-03-26   2.47   76.0   26.0  18.13\n",
       "0   501059     西部利得国企红利指数增强A(F501059)  2018-07-11   4.18  115.0   20.0  19.28\n",
       "6   005618         融通红利机会混合A(F005618)  2018-03-27   4.47   98.0   17.0  15.30\n",
       "5   004814         中欧红利优享混合A(F004814)  2018-04-19   4.41   69.0   13.0  25.26\n",
       "1   005561    创金合信中证红利低波动指数A(F005561)  2018-04-26   4.39   65.0   12.0  19.74\n",
       "7   080005          长盛量化红利混合(F080005)  2009-11-25  12.81  238.0   10.0  53.00\n",
       "2   161907    万家中证红利指数(LOF)A(F161907)  2011-03-17  11.50  136.0    8.0  46.84\n",
       "8   008163  南方大盘红利低波50ETF联接A(F008163)  2020-01-21   2.65   23.0    8.0  12.26\n",
       "3   501029            华宝红利基金(F501029)  2017-01-18   5.66   40.0    6.0  28.74\n",
       "10  007751    景顺长城中证沪港深红利低波A(F007751)  2019-09-06   3.02   14.0    4.0  19.54\n",
       "9   006658     财通中证香港红利等权指数A(F006658)  2019-04-26   3.39  -21.0   -7.0  29.02"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_df.sort_values(by='年化收益率',ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_df.to_excel('fund红利.xlsx',encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'001594'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def automatic_investment_plan(code):\n",
    "    '''\n",
    "    定投收益\n",
    "    '''\n",
    "    df = get_net_value_df(code)\n",
    "\n",
    "    money = 10000\n",
    "    total_share =0 \n",
    "    interval = 22\n",
    "    length = len(df)\n",
    "    sum_money=0\n",
    "    count=0\n",
    "    for i in range(0,length,interval):\n",
    "        buy_date_df = df.iloc[i]\n",
    "        share = money/buy_date_df['单位净值']\n",
    "        total_share+=share\n",
    "        sum_money+=money\n",
    "        count+=1\n",
    "    virtual_profit = (df.iloc[-1]['单位净值']*total_share-sum_money)/sum_money\n",
    "    data=[]\n",
    "    year,month,day=df.iloc[0]['净值日期'].split('-')\n",
    "    for i in range(count):\n",
    "        data.append((datetime.date(int(year), int(month), int(day))+datetime.timedelta(days=i*30), -1*money))\n",
    "    current_money = df.iloc[-1]['单位净值']*total_share\n",
    "    data.append((datetime.date(int(year), int(month), int(day))+datetime.timedelta(days=i*30),current_money))\n",
    "    percent = xirr(data)\n",
    "    every_round_profit = irr([money]*count+[-1*current_money])\n",
    "    real_profit = pow(every_round_profit+1,count)-1\n",
    "    return code,sum_money,round(current_money,2),round(percent,4),round(virtual_profit,4),round(real_profit,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def xirr(cashflows):\n",
    "    # 函数\n",
    "    years = [(ta[0] - cashflows[0][0]).days / 365. for ta in cashflows]\n",
    "    residual = 1.0\n",
    "    step = 0.05\n",
    "    guess = 0.05\n",
    "    epsilon = 0.0001\n",
    "    limit = 10000\n",
    "    while abs(residual) > epsilon and limit > 0:\n",
    "        limit -= 1\n",
    "        residual = 0.0\n",
    "        for i, trans in enumerate(cashflows):\n",
    "            residual += trans[1] / pow(guess, years[i])\n",
    "        if abs(residual) > epsilon:\n",
    "            if residual > 0:\n",
    "                guess += step\n",
    "            else:\n",
    "                guess -= step\n",
    "                step /= 2.0\n",
    "    return guess - 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试\n",
    "data = [(datetime.date(2006, 1, 24), -39967), (datetime.date(2008, 2, 6), -19866), (datetime.date(2010, 10, 18), 245706), (datetime.date(2013, 9, 14), 52142)]\n",
    "xirr(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "740000 891983.51\n",
      "001594 0.06 0.21 0.43\n",
      "740000 838011.42\n",
      "502010 0.04 0.13 0.27\n",
      "730000 548702.23\n",
      "167301 -0.1 -0.25 -0.45\n",
      "60000 62541.56\n",
      "013273 0.22 0.04 0.07\n",
      "140000 138765.67\n",
      "010696 -0.02 -0.01 -0.02\n"
     ]
    }
   ],
   "source": [
    "result =[]\n",
    "for code in code_list:\n",
    "    d={}\n",
    "    code,sum_money,current_money,percent,virtual_profit,real_profit=automatic_investment_plan(code)\n",
    "    d['代码']=code\n",
    "    d['投入总金额']=sum_money\n",
    "    d['目前总金额']=current_money\n",
    "    d['总收益']=percent\n",
    "    d['简单收益率']=virtual_profit\n",
    "    d['irr收益率']=real_profit\n",
    "    result.append(d)\n",
    "df = pd.DataFrame(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>代码</th>\n",
       "      <th>投入总金额</th>\n",
       "      <th>目前总金额</th>\n",
       "      <th>总收益</th>\n",
       "      <th>简单收益率</th>\n",
       "      <th>irr收益率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>001594</td>\n",
       "      <td>740000</td>\n",
       "      <td>891983.51</td>\n",
       "      <td>0.06</td>\n",
       "      <td>20.54</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>502010</td>\n",
       "      <td>740000</td>\n",
       "      <td>838011.42</td>\n",
       "      <td>0.04</td>\n",
       "      <td>13.24</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>167301</td>\n",
       "      <td>730000</td>\n",
       "      <td>548702.23</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-24.84</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>013273</td>\n",
       "      <td>60000</td>\n",
       "      <td>62541.56</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.24</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>010696</td>\n",
       "      <td>140000</td>\n",
       "      <td>138765.67</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       代码   投入总金额      目前总金额   总收益  简单收益率  irr收益率\n",
       "0  001594  740000  891983.51  0.06  20.54    0.43\n",
       "1  502010  740000  838011.42  0.04  13.24    0.27\n",
       "2  167301  730000  548702.23 -0.10 -24.84   -0.45\n",
       "3  013273   60000   62541.56  0.22   4.24    0.07\n",
       "4  010696  140000  138765.67 -0.02  -0.88   -0.02"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('定投.xlsx',encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def irr(values):\n",
    "    \"\"\"\n",
    "    Return the Internal Rate of Return (IRR).\n",
    "    .. deprecated:: 1.18\n",
    "       `irr` is deprecated; for details, see NEP 32 [1]_.\n",
    "       Use the corresponding function in the numpy-financial library,\n",
    "       https://pypi.org/project/numpy-financial.\n",
    "    This is the \"average\" periodically compounded rate of return\n",
    "    that gives a net present value of 0.0; for a more complete explanation,\n",
    "    see Notes below.\n",
    "    :class:`decimal.Decimal` type is not supported.\n",
    "    Parameters\n",
    "    ----------\n",
    "    values : array_like, shape(N,)\n",
    "        Input cash flows per time period.  By convention, net \"deposits\"\n",
    "        are negative and net \"withdrawals\" are positive.  Thus, for\n",
    "        example, at least the first element of `values`, which represents\n",
    "        the initial investment, will typically be negative.\n",
    "    Returns\n",
    "    -------\n",
    "    out : float\n",
    "        Internal Rate of Return for periodic input values.\n",
    "    Notes\n",
    "    -----\n",
    "    The IRR is perhaps best understood through an example (illustrated\n",
    "    using np.irr in the Examples section below).  Suppose one invests 100\n",
    "    units and then makes the following withdrawals at regular (fixed)\n",
    "    intervals: 39, 59, 55, 20.  Assuming the ending value is 0, one's 100\n",
    "    unit investment yields 173 units; however, due to the combination of\n",
    "    compounding and the periodic withdrawals, the \"average\" rate of return\n",
    "    is neither simply 0.73/4 nor (1.73)^0.25-1.  Rather, it is the solution\n",
    "    (for :math:`r`) of the equation:\n",
    "    .. math:: -100 + \\\\frac{39}{1+r} + \\\\frac{59}{(1+r)^2}\n",
    "     + \\\\frac{55}{(1+r)^3} + \\\\frac{20}{(1+r)^4} = 0\n",
    "    In general, for `values` :math:`= [v_0, v_1, ... v_M]`,\n",
    "    irr is the solution of the equation: [2]_\n",
    "    .. math:: \\\\sum_{t=0}^M{\\\\frac{v_t}{(1+irr)^{t}}} = 0\n",
    "    References\n",
    "    ----------\n",
    "    .. [1] NumPy Enhancement Proposal (NEP) 32,\n",
    "       https://numpy.org/neps/nep-0032-remove-financial-functions.html\n",
    "    .. [2] L. J. Gitman, \"Principles of Managerial Finance, Brief,\" 3rd ed.,\n",
    "       Addison-Wesley, 2003, pg. 348.\n",
    "    Examples\n",
    "    --------\n",
    "    >>> round(np.irr([-100, 39, 59, 55, 20]), 5)\n",
    "    0.28095\n",
    "    >>> round(np.irr([-100, 0, 0, 74]), 5)\n",
    "    -0.0955\n",
    "    >>> round(np.irr([-100, 100, 0, -7]), 5)\n",
    "    -0.0833\n",
    "    >>> round(np.irr([-100, 100, 0, 7]), 5)\n",
    "    0.06206\n",
    "    >>> round(np.irr([-5, 10.5, 1, -8, 1]), 5)\n",
    "    0.0886\n",
    "    \"\"\"\n",
    "    # `np.roots` call is why this function does not support Decimal type.\n",
    "    #\n",
    "    # Ultimately Decimal support needs to be added to np.roots, which has\n",
    "    # greater implications on the entire linear algebra module and how it does\n",
    "    # eigenvalue computations.\n",
    "    res = np.roots(values[::-1])  # 求根，对于n次多项式，p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]，传入p的列表参数[p[0],p[1],...p[n]].\n",
    "    mask = (res.imag == 0) & (res.real > 0)  # 虚部为0，实部为非负数。\n",
    "    if not mask.any():  # 判断是否有满足条件的实根\n",
    "        return np.nan  # 不满足，返回Not A Number\n",
    "    res = res[mask].real\n",
    "    # NPV(rate) = 0 can have more than one solution so we return\n",
    "    # only the solution closest to zero.\n",
    "    rate = 1/res - 1  # 这里解出的res，也就是符合条件的x，其实等于1/(1+r)，因此要做一个变换回去，r=1/x-1\n",
    "    rate = rate.item(np.argmin(np.abs(rate)))  # argmin()取最小值的下标，也就是说可能会计算出多个折现率，我们取最小那个\n",
    "    return rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "headers = {\n",
    "    'authority': 'api.jiucaishuo.com',\n",
    "    'sec-ch-ua': '\" Not A;Brand\";v=\"99\", \"Chromium\";v=\"98\", \"Google Chrome\";v=\"98\"',\n",
    "    'accept': 'application/json, text/plain, */*',\n",
    "    'content-type': 'application/json;charset=UTF-8',\n",
    "    'sec-ch-ua-mobile': '?0',\n",
    "    'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36',\n",
    "    'sec-ch-ua-platform': '\"Linux\"',\n",
    "    'origin': 'https://www.funddb.cn',\n",
    "    'sec-fetch-site': 'cross-site',\n",
    "    'sec-fetch-mode': 'cors',\n",
    "    'sec-fetch-dest': 'empty',\n",
    "    'accept-language': 'zh,en;q=0.9,en-US;q=0.8,zh-CN;q=0.7',\n",
    "}\n",
    "\n",
    "data = '{\"code\":\"013273\",\"category\":\"wind_category\",\"date\":\"\",\"type\":\"pc\",\"data_source\":\"xichou\",\"version\":\"1.8.9\",\"authtoken\":\"\",\"act_time\":1645634683356,\"tirgkjfs\":\"fb\",\"abiokytke\":\"52\",\"u54rg5d\":\"76\",\"kf54ge7\":\"f\",\"tiklsktr4\":\"b\",\"lksytkjh\":\"0ba1\",\"sbnoywr\":\"27\",\"bgd7h8tyu54\":\"46\",\"y654b5fs3tr\":\"d\",\"bioduytlw\":\"6\",\"bd4uy742\":\"4\",\"h67456y\":\"70b\",\"bvytikwqjk\":\"46\",\"ngd4uy551\":\"0b\",\"bgiuytkw\":\"bc\",\"nd354uy4752\":\"4\",\"ghtoiutkmlg\":\"db8\",\"bd24y6421f\":\"7e\",\"tbvdiuytk\":\"7\",\"ibvytiqjek\":\"1d\",\"jnhf8u5231\":\"bc\",\"fjlkatj\":\"760\",\"hy5641d321t\":\"e4\",\"iogojti\":\"e\",\"ngd4yut78\":\"b8\",\"nkjhrew\":\"4\",\"yt447e13f\":\"1\",\"n3bf4uj7y7\":\"b\",\"nbf4uj7y432\":\"52\",\"yi854tew\":\"54\",\"h13ey474\":\"54f\",\"quikgdky\":\"c3\"}'\n",
    "\n",
    "response = requests.post('https://api.jiucaishuo.com/v2/fund-lists/fundinvest', headers=headers, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['data']['gp']['list']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data_from_mongo(code):\n",
    "    result=[]\n",
    "    for item in fund[code].find():\n",
    "        result.append(item)\n",
    "\n",
    "    df = pd.DataFrame(result)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_data_from_mongo(code_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>净值日期</th>\n",
       "      <th>单位净值</th>\n",
       "      <th>日增长率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>621659cc28d482b10a0e5c05</td>\n",
       "      <td>2015-07-08</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>621659cc28d482b10a0e5c06</td>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>1.0815</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>621659cc28d482b10a0e5c07</td>\n",
       "      <td>2015-07-13</td>\n",
       "      <td>1.0514</td>\n",
       "      <td>-2.7832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>621659cc28d482b10a0e5c08</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>1.0324</td>\n",
       "      <td>-1.8071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>621659cc28d482b10a0e5c09</td>\n",
       "      <td>2015-07-15</td>\n",
       "      <td>1.0498</td>\n",
       "      <td>1.6854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id        净值日期    单位净值    日增长率\n",
       "0  621659cc28d482b10a0e5c05  2015-07-08  1.0000  0.0000\n",
       "1  621659cc28d482b10a0e5c06  2015-07-10  1.0815  0.0000\n",
       "2  621659cc28d482b10a0e5c07  2015-07-13  1.0514 -2.7832\n",
       "3  621659cc28d482b10a0e5c08  2015-07-14  1.0324 -1.8071\n",
       "4  621659cc28d482b10a0e5c09  2015-07-15  1.0498  1.6854"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = (\n",
    "    Scatter(InitOpts(width='800px',height='500px'))\n",
    "    .add_xaxis(simple_name)\n",
    "    .add_yaxis('A',focus_num,label_opts=opts.LabelOpts(is_show=True))\n",
    "    .set_global_opts(\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "                                    # name='日期',\n",
    "                                    # min_interval=5,\n",
    "                                    splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "                                            axislabel_opts=opts.LabelOpts(rotate=-45),\n",
    "\n",
    "                                    ),\n",
    "        yaxis_opts=opts.AxisOpts(    \n",
    "            min_=0,\n",
    "                splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "            ),\n",
    "        title_opts=opts.TitleOpts(title=\"红利基金收益率曲线\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(type_=\"size\",max_=150000, min_=2000),\n",
    "    )\n",
    "    .render(\"../plot_image/红利.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = (\n",
    "    Line()\n",
    "    .add_xaxis(X)\n",
    "    .add_yaxis(title, Y, is_smooth=True,\n",
    "    label_opts=opts.LabelOpts(is_show=False),\n",
    "linestyle_opts=opts.LineStyleOpts(width=2,color='rgb(255, 0, 0)'),\n",
    "    ).add_yaxis(title2, Y2, is_smooth=True,\n",
    "linestyle_opts=opts.LineStyleOpts(width=2,color='rgb(0, 0, 255)'),\n",
    "label_opts=opts.LabelOpts(is_show=False),\n",
    "    ).add_yaxis(title3, Y3, is_smooth=True,\n",
    "linestyle_opts=opts.LineStyleOpts(width=2,color='rgb(0, 255, 0)'),\n",
    "label_opts=opts.LabelOpts(is_show=False),\n",
    "    ).set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=full_title),\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "                                name='日期',\n",
    "                                min_interval=1,\n",
    "                                splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "           axislabel_opts=opts.LabelOpts(rotate=55),\n",
    "                                ),\n",
    "        yaxis_opts=opts.AxisOpts(name='收益率%',\n",
    "                                interval=3,\n",
    "                                 min_=y_min-2,\n",
    "                                 max_=y_max+2,\n",
    "            splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "        )\n",
    "                                    )\n",
    "                                    .set_colors(['red','blue','green']) # 点的颜色\n",
    "    .render(f\"../plot_image/多曲线plot_line_{name}_{types}_{date}_{rotation_rate}.html\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=Line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=result_na.index.tolist()\n",
    "X=list(map(lambda x:x.strftime('%Y-%m-%d'),X))\n",
    "c=c.add_xaxis(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for code,name in fund_dict.items():\n",
    "    Y=result_na[code].tolist()\n",
    "    c=c.add_yaxis(name, Y, is_smooth=True,\n",
    "    label_opts=opts.LabelOpts(is_show=False),\n",
    "linestyle_opts=opts.LineStyleOpts(width=1,color='rgb(255, 0, 0)'),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyecharts.charts.basic_charts.line.Line at 0x7fdd507d2eb0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title='ddddd'),\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "                                name='日期',\n",
    "                                # min_interval=1,\n",
    "                                splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "           axislabel_opts=opts.LabelOpts(rotate=55),\n",
    "                                ),\n",
    "        yaxis_opts=opts.AxisOpts(name='收益率%',\n",
    "                                # interval=3,\n",
    "                                 min_=0.6,\n",
    "                                 max_=2.8,\n",
    "            splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "        )\n",
    "                                    ).set_colors(['red','blue','green']) # 点的颜色\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/xda/github/stock/plot_image/多曲线plot_line_1111223.html'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render(f\"../plot_image/多曲线plot_line_1111223.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>118002</th>\n",
       "      <th>486002</th>\n",
       "      <th>006373</th>\n",
       "      <th>000369</th>\n",
       "      <th>100055</th>\n",
       "      <th>160416</th>\n",
       "      <th>320013</th>\n",
       "      <th>161815</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净值日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-20</th>\n",
       "      <td>1.702</td>\n",
       "      <td>2.058</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.601</td>\n",
       "      <td>1.3684</td>\n",
       "      <td>1.052</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-23</th>\n",
       "      <td>1.717</td>\n",
       "      <td>2.063</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>1.599</td>\n",
       "      <td>1.3795</td>\n",
       "      <td>1.035</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>1.726</td>\n",
       "      <td>2.158</td>\n",
       "      <td>1.0003</td>\n",
       "      <td>1.658</td>\n",
       "      <td>1.3908</td>\n",
       "      <td>1.059</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-07</th>\n",
       "      <td>1.668</td>\n",
       "      <td>2.053</td>\n",
       "      <td>1.0002</td>\n",
       "      <td>1.575</td>\n",
       "      <td>1.3383</td>\n",
       "      <td>1.031</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-14</th>\n",
       "      <td>1.675</td>\n",
       "      <td>2.043</td>\n",
       "      <td>1.0003</td>\n",
       "      <td>1.553</td>\n",
       "      <td>1.3255</td>\n",
       "      <td>1.008</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            118002  486002  006373  000369  100055  160416  320013  161815\n",
       "净值日期                                                                      \n",
       "2018-11-20   1.702   2.058  1.0000   1.601  1.3684   1.052   0.867   0.468\n",
       "2018-11-23   1.717   2.063  1.0001   1.599  1.3795   1.035   0.867   0.458\n",
       "2018-11-30   1.726   2.158  1.0003   1.658  1.3908   1.059   0.866   0.454\n",
       "2018-12-07   1.668   2.053  1.0002   1.575  1.3383   1.031   0.874   0.456\n",
       "2018-12-14   1.675   2.043  1.0003   1.553  1.3255   1.008   0.869   0.454"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_na.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = result_na.copy()\n",
    "new_df=recent_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "base=new_df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118002    2.0660\n",
       "486002    2.6780\n",
       "006373    1.8035\n",
       "000369    1.8460\n",
       "100055    2.1676\n",
       "160416    0.8760\n",
       "320013    1.1950\n",
       "161815    0.4190\n",
       "Name: 2020-07-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1887"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base['501059']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(x,code):\n",
    "    b=base[code]\n",
    "    return x/b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51200/2552348715.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['{}'.format(code)]=new_df[code].apply(convert,args=(code,))\n"
     ]
    }
   ],
   "source": [
    "for code,name in fund_dict.items():\n",
    "    new_df['{}'.format(code)]=new_df[code].apply(convert,args=(code,))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>501059</th>\n",
       "      <th>005561</th>\n",
       "      <th>161907</th>\n",
       "      <th>501029</th>\n",
       "      <th>008928</th>\n",
       "      <th>004814</th>\n",
       "      <th>005618</th>\n",
       "      <th>080005</th>\n",
       "      <th>008163</th>\n",
       "      <th>006658</th>\n",
       "      <th>007751</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净值日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>1.007824</td>\n",
       "      <td>1.005951</td>\n",
       "      <td>1.006891</td>\n",
       "      <td>1.006941</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.998462</td>\n",
       "      <td>1.001499</td>\n",
       "      <td>1.004869</td>\n",
       "      <td>1.006963</td>\n",
       "      <td>1.013702</td>\n",
       "      <td>1.006048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>1.003113</td>\n",
       "      <td>0.997662</td>\n",
       "      <td>0.995621</td>\n",
       "      <td>0.998107</td>\n",
       "      <td>1.0009</td>\n",
       "      <td>0.987591</td>\n",
       "      <td>1.010095</td>\n",
       "      <td>0.998229</td>\n",
       "      <td>0.997152</td>\n",
       "      <td>1.013237</td>\n",
       "      <td>1.003360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-10</th>\n",
       "      <td>1.015984</td>\n",
       "      <td>1.007545</td>\n",
       "      <td>1.004959</td>\n",
       "      <td>1.007572</td>\n",
       "      <td>1.0020</td>\n",
       "      <td>1.018049</td>\n",
       "      <td>1.036044</td>\n",
       "      <td>1.003984</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.047840</td>\n",
       "      <td>1.025874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>1.030369</td>\n",
       "      <td>1.015303</td>\n",
       "      <td>1.009338</td>\n",
       "      <td>1.009254</td>\n",
       "      <td>0.9952</td>\n",
       "      <td>1.024613</td>\n",
       "      <td>1.065620</td>\n",
       "      <td>1.007525</td>\n",
       "      <td>1.007596</td>\n",
       "      <td>1.053530</td>\n",
       "      <td>1.029794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              501059    005561    161907    501029  008928    004814  \\\n",
       "净值日期                                                                   \n",
       "2020-03-26  1.000000  1.000000  1.000000  1.000000  1.0000  1.000000   \n",
       "2020-03-27  1.007824  1.005951  1.006891  1.006941  1.0000  0.998462   \n",
       "2020-04-03  1.003113  0.997662  0.995621  0.998107  1.0009  0.987591   \n",
       "2020-04-10  1.015984  1.007545  1.004959  1.007572  1.0020  1.018049   \n",
       "2020-04-17  1.030369  1.015303  1.009338  1.009254  0.9952  1.024613   \n",
       "\n",
       "              005618    080005    008163    006658    007751  \n",
       "净值日期                                                          \n",
       "2020-03-26  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "2020-03-27  1.001499  1.004869  1.006963  1.013702  1.006048  \n",
       "2020-04-03  1.010095  0.998229  0.997152  1.013237  1.003360  \n",
       "2020-04-10  1.036044  1.003984  1.001055  1.047840  1.025874  \n",
       "2020-04-17  1.065620  1.007525  1.007596  1.053530  1.029794  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_na1=new_df.rename(columns=fund_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_na1.to_pickle('obj_test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_na1 = pd.read_pickle('obj_test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>易方达标普消费品</th>\n",
       "      <th>工银全球精选股票</th>\n",
       "      <th>国富全球科技互联</th>\n",
       "      <th>广发全球医疗保健</th>\n",
       "      <th>富国全球科技互联</th>\n",
       "      <th>华安标普全球石油</th>\n",
       "      <th>诺安全球黄金</th>\n",
       "      <th>银华抗通胀主题</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净值日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-02</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>1.023717</td>\n",
       "      <td>1.017924</td>\n",
       "      <td>1.027003</td>\n",
       "      <td>1.009751</td>\n",
       "      <td>1.028188</td>\n",
       "      <td>1.003425</td>\n",
       "      <td>1.005021</td>\n",
       "      <td>1.009547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>1.015489</td>\n",
       "      <td>1.004481</td>\n",
       "      <td>1.007430</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>1.008396</td>\n",
       "      <td>0.981735</td>\n",
       "      <td>1.005858</td>\n",
       "      <td>1.002387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>1.013069</td>\n",
       "      <td>1.013443</td>\n",
       "      <td>1.029498</td>\n",
       "      <td>0.994041</td>\n",
       "      <td>1.038522</td>\n",
       "      <td>0.977169</td>\n",
       "      <td>1.012552</td>\n",
       "      <td>1.009547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-09</th>\n",
       "      <td>1.015973</td>\n",
       "      <td>1.012696</td>\n",
       "      <td>1.058608</td>\n",
       "      <td>0.986457</td>\n",
       "      <td>1.064449</td>\n",
       "      <td>0.950913</td>\n",
       "      <td>1.005858</td>\n",
       "      <td>0.997613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            易方达标普消费品  工银全球精选股票  国富全球科技互联  广发全球医疗保健  富国全球科技互联  华安标普全球石油  \\\n",
       "净值日期                                                                     \n",
       "2020-07-02  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "2020-07-06  1.023717  1.017924  1.027003  1.009751  1.028188  1.003425   \n",
       "2020-07-07  1.015489  1.004481  1.007430  0.996750  1.008396  0.981735   \n",
       "2020-07-08  1.013069  1.013443  1.029498  0.994041  1.038522  0.977169   \n",
       "2020-07-09  1.015973  1.012696  1.058608  0.986457  1.064449  0.950913   \n",
       "\n",
       "              诺安全球黄金   银华抗通胀主题  \n",
       "净值日期                            \n",
       "2020-07-02  1.000000  1.000000  \n",
       "2020-07-06  1.005021  1.009547  \n",
       "2020-07-07  1.005858  1.002387  \n",
       "2020-07-08  1.012552  1.009547  \n",
       "2020-07-09  1.005858  0.997613  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_na1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_na1.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df-obj']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(result_na1,'df-obj')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (16.0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='净值日期'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20928 (\\N{CJK UNIFIED IDEOGRAPH-51C0}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26131 (\\N{CJK UNIFIED IDEOGRAPH-6613}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26041 (\\N{CJK UNIFIED IDEOGRAPH-65B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36798 (\\N{CJK UNIFIED IDEOGRAPH-8FBE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 26222 (\\N{CJK UNIFIED IDEOGRAPH-666E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 28040 (\\N{CJK UNIFIED IDEOGRAPH-6D88}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36153 (\\N{CJK UNIFIED IDEOGRAPH-8D39}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21697 (\\N{CJK UNIFIED IDEOGRAPH-54C1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 24037 (\\N{CJK UNIFIED IDEOGRAPH-5DE5}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 38134 (\\N{CJK UNIFIED IDEOGRAPH-94F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20840 (\\N{CJK UNIFIED IDEOGRAPH-5168}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 29699 (\\N{CJK UNIFIED IDEOGRAPH-7403}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31934 (\\N{CJK UNIFIED IDEOGRAPH-7CBE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36873 (\\N{CJK UNIFIED IDEOGRAPH-9009}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 22269 (\\N{CJK UNIFIED IDEOGRAPH-56FD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 23500 (\\N{CJK UNIFIED IDEOGRAPH-5BCC}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20114 (\\N{CJK UNIFIED IDEOGRAPH-4E92}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32852 (\\N{CJK UNIFIED IDEOGRAPH-8054}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 24191 (\\N{CJK UNIFIED IDEOGRAPH-5E7F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21457 (\\N{CJK UNIFIED IDEOGRAPH-53D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21307 (\\N{CJK UNIFIED IDEOGRAPH-533B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30103 (\\N{CJK UNIFIED IDEOGRAPH-7597}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20445 (\\N{CJK UNIFIED IDEOGRAPH-4FDD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20581 (\\N{CJK UNIFIED IDEOGRAPH-5065}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 21326 (\\N{CJK UNIFIED IDEOGRAPH-534E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 23433 (\\N{CJK UNIFIED IDEOGRAPH-5B89}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30707 (\\N{CJK UNIFIED IDEOGRAPH-77F3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 27833 (\\N{CJK UNIFIED IDEOGRAPH-6CB9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 35834 (\\N{CJK UNIFIED IDEOGRAPH-8BFA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 40644 (\\N{CJK UNIFIED IDEOGRAPH-9EC4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 37329 (\\N{CJK UNIFIED IDEOGRAPH-91D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 25239 (\\N{CJK UNIFIED IDEOGRAPH-6297}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36890 (\\N{CJK UNIFIED IDEOGRAPH-901A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32960 (\\N{CJK UNIFIED IDEOGRAPH-80C0}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20027 (\\N{CJK UNIFIED IDEOGRAPH-4E3B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/xda/miniconda3/envs/cpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 39064 (\\N{CJK UNIFIED IDEOGRAPH-9898}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(8,8))\n",
    "\n",
    "result_na1.plot(grid=True,)\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for code,name in fund_dict.items():\n",
    "    result = []\n",
    "    for item in fund[code].find({},{'_id':0}):\n",
    "        result.append(item)\n",
    "    df = pd.DataFrame(result)\n",
    "    plot_profit_line(df,name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fund_dict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb Cell 89'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb#ch0000077?line=0'>1</a>\u001b[0m fund_dict\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fund_dict' is not defined"
     ]
    }
   ],
   "source": [
    "fund_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'占净值比例'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3619'>3620</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3620'>3621</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3621'>3622</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '占净值比例'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb Cell 90'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb#ch0000125?line=0'>1</a>\u001b[0m fund_portfolio_hold_em_df \u001b[39m=\u001b[39m ak\u001b[39m.\u001b[39;49mfund_portfolio_hold_em(code\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m000001\u001b[39;49m\u001b[39m\"\u001b[39;49m, year\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m2023\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_em_portfolio.py:63\u001b[0m, in \u001b[0;36mfund_portfolio_hold_em\u001b[0;34m(code, year)\u001b[0m\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3502'>3503</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3503'>3504</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3504'>3505</a>\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3505'>3506</a>\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3506'>3507</a>\u001b[0m     indexer \u001b[39m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3620'>3621</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3621'>3622</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3622'>3623</a>\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3623'>3624</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3624'>3625</a>\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3625'>3626</a>\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3626'>3627</a>\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=3627'>3628</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: '占净值比例'"
     ]
    }
   ],
   "source": [
    "fund_portfolio_hold_em_df = ak.fund_portfolio_hold_em(code=\"000001\", year=\"2023\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import akshare as ak\n",
    "def get_holding_list(code,year):\n",
    "    fund_portfolio_hold_em_df = ak.fund_portfolio_hold_em(symbol=code, date=year)\n",
    "    return fund_portfolio_hold_em_df[fund_portfolio_hold_em_df['季度']=='2022年4季度股票投资明细']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "code='501059'\n",
    "holding_dict[code] = get_holding_list(code,2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_list = holding_dict[code]['股票名称'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "holding_dict={}\n",
    "for code,name in fund_dict.items():\n",
    "    holding_dict[code] = get_holding_list(code,2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>股票代码</th>\n",
       "      <th>股票名称</th>\n",
       "      <th>占净值比例</th>\n",
       "      <th>持股数</th>\n",
       "      <th>持仓市值</th>\n",
       "      <th>季度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>600438</td>\n",
       "      <td>通威股份</td>\n",
       "      <td>6.50</td>\n",
       "      <td>140.00</td>\n",
       "      <td>8380.40</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>300498</td>\n",
       "      <td>温氏股份</td>\n",
       "      <td>6.29</td>\n",
       "      <td>380.85</td>\n",
       "      <td>8108.40</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>000902</td>\n",
       "      <td>新洋丰</td>\n",
       "      <td>5.44</td>\n",
       "      <td>415.00</td>\n",
       "      <td>7009.35</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>002714</td>\n",
       "      <td>牧原股份</td>\n",
       "      <td>5.43</td>\n",
       "      <td>126.55</td>\n",
       "      <td>6994.32</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>002250</td>\n",
       "      <td>联化科技</td>\n",
       "      <td>5.08</td>\n",
       "      <td>400.00</td>\n",
       "      <td>6548.00</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>124</td>\n",
       "      <td>001268</td>\n",
       "      <td>联合精密</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>125</td>\n",
       "      <td>301111</td>\n",
       "      <td>粤万年青</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>126</td>\n",
       "      <td>301138</td>\n",
       "      <td>华研精机</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.88</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>301158</td>\n",
       "      <td>德石股份</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>128</td>\n",
       "      <td>301198</td>\n",
       "      <td>喜悦智行</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2022年2季度股票投资明细</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      序号    股票代码  股票名称  占净值比例     持股数     持仓市值              季度\n",
       "0      1  600438  通威股份   6.50  140.00  8380.40  2022年2季度股票投资明细\n",
       "1      2  300498  温氏股份   6.29  380.85  8108.40  2022年2季度股票投资明细\n",
       "2      3  000902   新洋丰   5.44  415.00  7009.35  2022年2季度股票投资明细\n",
       "3      4  002714  牧原股份   5.43  126.55  6994.32  2022年2季度股票投资明细\n",
       "4      5  002250  联化科技   5.08  400.00  6548.00  2022年2季度股票投资明细\n",
       "..   ...     ...   ...    ...     ...      ...             ...\n",
       "123  124  001268  联合精密   0.00    0.04     1.13  2022年2季度股票投资明细\n",
       "124  125  301111  粤万年青   0.00    0.04     1.04  2022年2季度股票投资明细\n",
       "125  126  301138  华研精机   0.00    0.03     0.88  2022年2季度股票投资明细\n",
       "126  127  301158  德石股份   0.00    0.04     0.77  2022年2季度股票投资明细\n",
       "127  128  301198  喜悦智行   0.00    0.03     0.77  2022年2季度股票投资明细\n",
       "\n",
       "[128 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holding_dict['001579']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_lists = []\n",
    "for code,holding in holding_dict.items():\n",
    "    stock_list = holding['股票名称'].tolist()\n",
    "    stock_lists.extend(stock_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "519198\n",
      "000991\n",
      "001869\n",
      "001220\n"
     ]
    }
   ],
   "source": [
    "for code,holding in holding_dict.items():\n",
    "    x=holding['股票名称'].tolist()\n",
    "    \n",
    "    if '贵州茅台' not in x:\n",
    "        print(code)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'001667':    序号    股票代码  股票名称  占净值比例     持股数      持仓市值              季度\n",
       " 0   1  600519  贵州茅台   2.73    7.51  12964.59  2022年4季度股票投资明细\n",
       " 1   2  601166  兴业银行   2.52  680.01  11961.32  2022年4季度股票投资明细\n",
       " 2   3  002531  天顺风能   2.20  690.00  10439.70  2022年4季度股票投资明细\n",
       " 3   4  688599  天合光能   2.15  160.00  10201.60  2022年4季度股票投资明细\n",
       " 4   5  000001  平安银行   2.11  760.00  10001.61  2022年4季度股票投资明细\n",
       " 5   6  600176  中国巨石   2.02  700.00   9597.01  2022年4季度股票投资明细\n",
       " 6   7  600660  福耀玻璃   2.00  270.01   9469.18  2022年4季度股票投资明细\n",
       " 7   8  601877  正泰电器   1.93  330.00   9141.00  2022年4季度股票投资明细\n",
       " 8   9  600346  恒力石化   1.83  560.01   8696.90  2022年4季度股票投资明细\n",
       " 9  10  600887  伊利股份   1.83  280.00   8680.00  2022年4季度股票投资明细,\n",
       " '001832':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  000596  古井贡酒   9.42   131.99  35229.17  2022年4季度股票投资明细\n",
       " 1   2  000933  神火股份   7.90  1975.90  29559.46  2022年4季度股票投资明细\n",
       " 2   3  600519  贵州茅台   7.84    16.99  29344.49  2022年4季度股票投资明细\n",
       " 3   4  601899  紫金矿业   7.23  2705.50  27055.03  2022年4季度股票投资明细\n",
       " 4   5  600188  兖矿能源   7.17   798.74  26821.74  2022年4季度股票投资明细\n",
       " 5   6  601699  潞安环能   5.66  1255.95  21162.77  2022年4季度股票投资明细\n",
       " 6   7  600546  山煤国际   5.47  1412.31  20464.44  2022年4季度股票投资明细\n",
       " 7   8  000858   五粮液   5.42   112.25  20283.34  2022年4季度股票投资明细\n",
       " 8   9  600685  中船防务   4.29   773.11  16049.76  2022年4季度股票投资明细\n",
       " 9  10  000983  山西焦煤   3.74  1201.88  14001.86  2022年4季度股票投资明细,\n",
       " '001018':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  002180   纳思达   5.62   801.95  41613.09  2022年4季度股票投资明细\n",
       " 1   2  600519  贵州茅台   5.49    23.55  40675.17  2022年4季度股票投资明细\n",
       " 2   3  300068  南都电源   4.94  1717.92  36591.78  2022年4季度股票投资明细\n",
       " 3   4  000858   五粮液   3.92   160.78  29051.95  2022年4季度股票投资明细\n",
       " 4   5  002594   比亚迪   3.87   111.68  28699.21  2022年4季度股票投资明细\n",
       " 5   6  600845  宝信软件   3.52   582.29  26086.65  2022年4季度股票投资明细\n",
       " 6   7  002049  紫光国微   2.71   152.36  20084.40  2022年4季度股票投资明细\n",
       " 7   8  688066  航天宏图   2.63   228.60  19477.66  2022年4季度股票投资明细\n",
       " 8   9  002756  永兴材料   2.47   198.87  18329.59  2022年4季度股票投资明细\n",
       " 9  10  300750  宁德时代   2.37    44.68  17577.22  2022年4季度股票投资明细,\n",
       " '519002':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  600519  贵州茅台   5.83    24.54  42384.38  2022年4季度股票投资明细\n",
       " 1   2  600027  华电国际   3.38  4178.88  24571.82  2022年4季度股票投资明细\n",
       " 2   3  600754  锦江酒店   3.14   390.43  22781.59  2022年4季度股票投资明细\n",
       " 3   4  601816  京沪高铁   2.97  4381.34  21556.19  2022年4季度股票投资明细\n",
       " 4   5  600559  老白干酒   2.86   755.13  20788.73  2022年4季度股票投资明细\n",
       " 5   6  600029  南方航空   2.47  2360.01  17936.09  2022年4季度股票投资明细\n",
       " 6   7  600690  海尔智家   2.25   669.56  16377.35  2022年4季度股票投资明细\n",
       " 7   8  300026  红日药业   2.21  2828.60  16066.47  2022年4季度股票投资明细\n",
       " 8   9  600583  海油工程   2.17  2603.20  15775.39  2022年4季度股票投资明细\n",
       " 9  10  601628  中国人寿   2.17   424.53  15758.55  2022年4季度股票投资明细,\n",
       " '519198':    序号    股票代码  股票名称  占净值比例     持股数     持仓市值              季度\n",
       " 0   1  601225  陕西煤业   9.10  504.82  9379.62  2022年4季度股票投资明细\n",
       " 1   2  600021  上海电力   8.68  893.48  8943.71  2022年4季度股票投资明细\n",
       " 2   3  601088  中国神华   8.59  320.52  8852.74  2022年4季度股票投资明细\n",
       " 3   4  601898  中煤能源   8.31  993.48  8563.80  2022年4季度股票投资明细\n",
       " 4   5  000776  广发证券   7.34  488.23  7562.68  2022年4季度股票投资明细\n",
       " 5   6  600999  招商证券   6.28  487.15  6479.14  2022年4季度股票投资明细\n",
       " 6   7  600030  中信证券   5.89  305.20  6076.49  2022年4季度股票投资明细\n",
       " 7   8  601001  晋控煤业   5.70  491.09  5873.44  2022年4季度股票投资明细\n",
       " 8   9  600985  淮北矿业   4.94  397.94  5093.63  2022年4季度股票投资明细\n",
       " 9  10  600905  三峡能源   3.95  721.01  4073.71  2022年4季度股票投资明细,\n",
       " '450004':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  600256  广汇能源   3.00  2194.03  19790.16  2022年4季度股票投资明细\n",
       " 1   2  300274  阳光电源   2.96   175.00  19565.23  2022年4季度股票投资明细\n",
       " 2   3  002142  宁波银行   2.89   587.71  19071.19  2022年4季度股票投资明细\n",
       " 3   4  002050  三花智控   2.83   879.03  18652.99  2022年4季度股票投资明细\n",
       " 4   5  002648  卫星化学   2.73  1160.77  17991.94  2022年4季度股票投资明细\n",
       " 5   6  600519  贵州茅台   2.72    10.41  17979.62  2022年4季度股票投资明细\n",
       " 6   7  601100  恒立液压   2.34   244.30  15427.64  2022年4季度股票投资明细\n",
       " 7   8  002475  立讯精密   2.31   479.19  15214.25  2022年4季度股票投资明细\n",
       " 8   9  601658  邮储银行   2.28  3258.53  15054.41  2022年4季度股票投资明细\n",
       " 9  10  600309  万华化学   2.18   155.33  14391.07  2022年4季度股票投资明细,\n",
       " '000991':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  601668  中国建筑   9.71  6598.42  35829.41  2022年4季度股票投资明细\n",
       " 1   2  600048  保利发展   8.00  1951.56  29527.10  2022年4季度股票投资明细\n",
       " 2   3  601166  兴业银行   7.85  1647.94  28987.28  2022年4季度股票投资明细\n",
       " 3   4  601009  南京银行   5.74  2034.70  21201.56  2022年4季度股票投资明细\n",
       " 4   5  001979  招商蛇口   5.23  1529.03  19311.65  2022年4季度股票投资明细\n",
       " 5   6  601818  光大银行   4.65  5596.75  17182.02  2022年4季度股票投资明细\n",
       " 6   7  600383  金地集团   4.55  1643.47  16812.70  2022年4季度股票投资明细\n",
       " 7   8  601838  成都银行   4.17  1007.35  15412.51  2022年4季度股票投资明细\n",
       " 8   9  600926  杭州银行   4.10  1155.98  15120.22  2022年4季度股票投资明细\n",
       " 9  10  600919  江苏银行   3.58  1811.77  13207.84  2022年4季度股票投资明细,\n",
       " '001869':    序号    股票代码  股票名称  占净值比例     持股数      持仓市值              季度\n",
       " 0   1  600438  通威股份   7.75  569.56  21973.64  2022年4季度股票投资明细\n",
       " 1   2  300496  中科创达   6.09  172.24  17275.93  2022年4季度股票投资明细\n",
       " 2   3  300122  智飞生物   6.07  196.06  17219.68  2022年4季度股票投资明细\n",
       " 3   4  000963  华东医药   5.38  326.17  15264.85  2022年4季度股票投资明细\n",
       " 4   5  600426  华鲁恒升   5.24  448.60  14871.09  2022年4季度股票投资明细\n",
       " 5   6  002475  立讯精密   4.99  445.72  14151.59  2022年4季度股票投资明细\n",
       " 6   7  002223  鱼跃医疗   4.19  373.41  11896.87  2022年4季度股票投资明细\n",
       " 7   8  601888  中国中免   4.08   53.51  11559.53  2022年4季度股票投资明细\n",
       " 8   9  300450  先导智能   4.07  286.97  11550.59  2022年4季度股票投资明细\n",
       " 9  10  000733  振华科技   3.86   95.86  10950.60  2022年4季度股票投资明细,\n",
       " '110015':    序号    股票代码  股票名称  占净值比例     持股数      持仓市值              季度\n",
       " 0   1  600519  贵州茅台   7.30    8.01  13832.06  2022年4季度股票投资明细\n",
       " 1   2  601012  隆基绿能   7.15  320.87  13559.86  2022年4季度股票投资明细\n",
       " 2   3  600809  山西汾酒   5.84   38.81  11060.35  2022年4季度股票投资明细\n",
       " 3   4  000568  泸州老窖   5.34   45.12  10119.24  2022年4季度股票投资明细\n",
       " 4   5  688639  华恒生物   4.45   54.27   8425.50  2022年4季度股票投资明细\n",
       " 5   6  002049  紫光国微   4.16   59.85   7889.41  2022年4季度股票投资明细\n",
       " 6   7  600233  圆通速递   4.10  386.35   7761.78  2022年4季度股票投资明细\n",
       " 7   8  000858   五粮液   3.75   39.38   7116.35  2022年4季度股票投资明细\n",
       " 8   9  603806   福斯特   3.36   95.85   6368.41  2022年4季度股票投资明细\n",
       " 9  10  300628  亿联网络   3.20  100.22   6072.44  2022年4季度股票投资明细,\n",
       " '001220':    序号    股票代码  股票名称  占净值比例     持股数     持仓市值              季度\n",
       " 0   1  688363  华熙生物   4.89   37.49  5071.17  2022年4季度股票投资明细\n",
       " 1   2  300015  爱尔眼科   4.62  154.23  4791.93  2022年4季度股票投资明细\n",
       " 2   3  300957   贝泰妮   4.58   31.86  4754.79  2022年4季度股票投资明细\n",
       " 3   4  600763  通策医疗   4.54   30.83  4716.68  2022年4季度股票投资明细\n",
       " 4   5  300896   爱美客   4.44    8.14  4610.09  2022年4季度股票投资明细\n",
       " 5   6  600298  安琪酵母   4.30   98.79  4467.28  2022年4季度股票投资明细\n",
       " 6   7  603288  海天味业   3.81   49.74  3959.44  2022年4季度股票投资明细\n",
       " 7   8  603345  安井食品   3.81   24.42  3953.11  2022年4季度股票投资明细\n",
       " 8   9  600887  伊利股份   3.57  119.44  3702.64  2022年4季度股票投资明细\n",
       " 9  10  603605   珀莱雅   3.38   20.94  3506.53  2022年4季度股票投资明细}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holding_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_dict ={}\n",
    "for i in stock_lists:\n",
    "    count_dict.setdefault(i,0)\n",
    "    count_dict[i]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "holding_ordered = sorted(count_dict.items(),key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "724"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stock_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "贵州茅台 6\n",
      "五粮液 3\n",
      "兴业银行 2\n",
      "伊利股份 2\n",
      "紫光国微 2\n",
      "立讯精密 2\n"
     ]
    }
   ],
   "source": [
    "for  i in holding_ordered:\n",
    "    if i[1]>=2:\n",
    "        print(i[0],i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    排名   板块名称    板块代码       最新价     涨跌额   涨跌幅            总市值   换手率  上涨家数  \\\n",
      "0    1   通信服务  BK0736    925.33   37.86  4.27  2808561568000  4.29    41   \n",
      "1    2  互联网服务  BK0447  19736.87  756.98  3.99  1616366112000  5.21   131   \n",
      "2    3   软件开发  BK0737    849.00   29.68  3.62  2121979408000  5.25   164   \n",
      "3    4     游戏  BK1046   1047.38   33.53  3.31   340843664000  5.21    30   \n",
      "4    5  计算机设备  BK0735    984.85   27.21  2.84   920453120000  4.70    38   \n",
      "..  ..    ...     ...       ...     ...   ...            ...   ...   ...   \n",
      "81  82   医药商业  BK1042   1170.64   -4.06 -0.35   422802976000  1.19    10   \n",
      "82  83   医疗器械  BK1041    986.08   -3.78 -0.38  1846142608000  1.21    48   \n",
      "83  84   生物制品  BK1044    943.65   -4.47 -0.47  1327821648000  0.99    17   \n",
      "84  85   风电设备  BK1032   1372.86   -7.07 -0.51   397853920000  0.96     6   \n",
      "85  86   光伏设备  BK1031   2035.92  -33.97 -1.64  2203307024000  1.40    14   \n",
      "\n",
      "    下跌家数  领涨股票  领涨股票-涨跌幅  \n",
      "0      0  中通国脉     10.02  \n",
      "1     11  金桥信息     10.00  \n",
      "2     11  网达软件     10.01  \n",
      "3      0  神州泰岳      9.74  \n",
      "4     10  苏州科达      9.98  \n",
      "..   ...   ...       ...  \n",
      "81    17  柳药集团      3.71  \n",
      "82    65  康众医疗     10.35  \n",
      "83    49   英诺特      7.50  \n",
      "84    14  三一重能      3.44  \n",
      "85    32  芯能科技      1.43  \n",
      "\n",
      "[86 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "import akshare as ak\n",
    "stock_board_industry_name_em_df = ak.stock_board_industry_name_em()\n",
    "print(stock_board_industry_name_em_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bk_name = stock_board_industry_name_em_df['板块名称'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'通信服务'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bk_name[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import akshare as ak\n",
    "bk_result ={}\n",
    "for bk in bk_name:\n",
    "    stock_board_industry_cons_em_df = ak.stock_board_industry_cons_em(symbol=bk)\n",
    "    st_name =stock_board_industry_cons_em_df['名称'].tolist()\n",
    "    # st=['']\n",
    "    bk_result[bk]=st_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>代码</th>\n",
       "      <th>名称</th>\n",
       "      <th>最新价</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>今开</th>\n",
       "      <th>昨收</th>\n",
       "      <th>换手率</th>\n",
       "      <th>市盈率-动态</th>\n",
       "      <th>市净率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>603105</td>\n",
       "      <td>芯能科技</td>\n",
       "      <td>17.72</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.25</td>\n",
       "      <td>138796</td>\n",
       "      <td>2.432346e+08</td>\n",
       "      <td>2.58</td>\n",
       "      <td>17.75</td>\n",
       "      <td>17.30</td>\n",
       "      <td>17.50</td>\n",
       "      <td>17.47</td>\n",
       "      <td>2.78</td>\n",
       "      <td>41.32</td>\n",
       "      <td>5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>300393</td>\n",
       "      <td>中来股份</td>\n",
       "      <td>17.18</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.19</td>\n",
       "      <td>270884</td>\n",
       "      <td>4.590574e+08</td>\n",
       "      <td>2.83</td>\n",
       "      <td>17.20</td>\n",
       "      <td>16.72</td>\n",
       "      <td>16.99</td>\n",
       "      <td>16.99</td>\n",
       "      <td>2.84</td>\n",
       "      <td>40.92</td>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>002129</td>\n",
       "      <td>TCL中环</td>\n",
       "      <td>44.12</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.47</td>\n",
       "      <td>258274</td>\n",
       "      <td>1.135493e+09</td>\n",
       "      <td>1.95</td>\n",
       "      <td>44.38</td>\n",
       "      <td>43.53</td>\n",
       "      <td>43.86</td>\n",
       "      <td>43.65</td>\n",
       "      <td>0.80</td>\n",
       "      <td>21.38</td>\n",
       "      <td>3.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>835985</td>\n",
       "      <td>海泰新能</td>\n",
       "      <td>8.94</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>12233</td>\n",
       "      <td>1.082701e+07</td>\n",
       "      <td>2.71</td>\n",
       "      <td>8.95</td>\n",
       "      <td>8.71</td>\n",
       "      <td>8.89</td>\n",
       "      <td>8.85</td>\n",
       "      <td>0.91</td>\n",
       "      <td>29.37</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>301046</td>\n",
       "      <td>能辉科技</td>\n",
       "      <td>40.70</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.30</td>\n",
       "      <td>29262</td>\n",
       "      <td>1.179232e+08</td>\n",
       "      <td>2.20</td>\n",
       "      <td>40.80</td>\n",
       "      <td>39.91</td>\n",
       "      <td>40.46</td>\n",
       "      <td>40.40</td>\n",
       "      <td>4.96</td>\n",
       "      <td>311.92</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>300029</td>\n",
       "      <td>ST天龙</td>\n",
       "      <td>8.29</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.04</td>\n",
       "      <td>19548</td>\n",
       "      <td>1.617148e+07</td>\n",
       "      <td>1.70</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.19</td>\n",
       "      <td>8.25</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0.97</td>\n",
       "      <td>35.22</td>\n",
       "      <td>32.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>300317</td>\n",
       "      <td>珈伟新能</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.03</td>\n",
       "      <td>228560</td>\n",
       "      <td>1.415638e+08</td>\n",
       "      <td>1.93</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.22</td>\n",
       "      <td>2.95</td>\n",
       "      <td>41.43</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>600207</td>\n",
       "      <td>安彩高科</td>\n",
       "      <td>6.72</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.03</td>\n",
       "      <td>64537</td>\n",
       "      <td>4.306403e+07</td>\n",
       "      <td>1.64</td>\n",
       "      <td>6.73</td>\n",
       "      <td>6.62</td>\n",
       "      <td>6.70</td>\n",
       "      <td>6.69</td>\n",
       "      <td>0.75</td>\n",
       "      <td>53.12</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>002610</td>\n",
       "      <td>爱康科技</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.01</td>\n",
       "      <td>431486</td>\n",
       "      <td>1.225051e+08</td>\n",
       "      <td>1.41</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.84</td>\n",
       "      <td>0.98</td>\n",
       "      <td>-35.17</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>603628</td>\n",
       "      <td>清源股份</td>\n",
       "      <td>16.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20764</td>\n",
       "      <td>3.453946e+07</td>\n",
       "      <td>1.26</td>\n",
       "      <td>16.75</td>\n",
       "      <td>16.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>16.70</td>\n",
       "      <td>0.76</td>\n",
       "      <td>50.36</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>688303</td>\n",
       "      <td>大全能源</td>\n",
       "      <td>48.07</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>72410</td>\n",
       "      <td>3.477293e+08</td>\n",
       "      <td>0.86</td>\n",
       "      <td>48.17</td>\n",
       "      <td>47.76</td>\n",
       "      <td>47.95</td>\n",
       "      <td>47.93</td>\n",
       "      <td>1.43</td>\n",
       "      <td>5.37</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>603185</td>\n",
       "      <td>上机数控</td>\n",
       "      <td>111.27</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>28052</td>\n",
       "      <td>3.115888e+08</td>\n",
       "      <td>1.46</td>\n",
       "      <td>111.80</td>\n",
       "      <td>110.18</td>\n",
       "      <td>111.00</td>\n",
       "      <td>111.07</td>\n",
       "      <td>0.69</td>\n",
       "      <td>12.11</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>002617</td>\n",
       "      <td>露笑科技</td>\n",
       "      <td>8.92</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.01</td>\n",
       "      <td>205556</td>\n",
       "      <td>1.829574e+08</td>\n",
       "      <td>0.67</td>\n",
       "      <td>8.93</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.92</td>\n",
       "      <td>8.91</td>\n",
       "      <td>1.27</td>\n",
       "      <td>-217.24</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>688516</td>\n",
       "      <td>奥特维</td>\n",
       "      <td>196.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>6425</td>\n",
       "      <td>1.248727e+08</td>\n",
       "      <td>2.14</td>\n",
       "      <td>196.78</td>\n",
       "      <td>192.59</td>\n",
       "      <td>195.99</td>\n",
       "      <td>195.90</td>\n",
       "      <td>0.85</td>\n",
       "      <td>43.40</td>\n",
       "      <td>11.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>600151</td>\n",
       "      <td>航天机电</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>78211</td>\n",
       "      <td>7.790840e+07</td>\n",
       "      <td>1.10</td>\n",
       "      <td>10.02</td>\n",
       "      <td>9.91</td>\n",
       "      <td>9.95</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-1160.06</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>002506</td>\n",
       "      <td>协鑫集成</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>427841</td>\n",
       "      <td>1.349946e+08</td>\n",
       "      <td>1.26</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.73</td>\n",
       "      <td>-137.44</td>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>600438</td>\n",
       "      <td>通威股份</td>\n",
       "      <td>41.88</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>425909</td>\n",
       "      <td>1.783328e+09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>42.24</td>\n",
       "      <td>41.65</td>\n",
       "      <td>41.95</td>\n",
       "      <td>41.90</td>\n",
       "      <td>0.95</td>\n",
       "      <td>6.51</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>601012</td>\n",
       "      <td>隆基绿能</td>\n",
       "      <td>44.14</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>481804</td>\n",
       "      <td>2.126099e+09</td>\n",
       "      <td>1.27</td>\n",
       "      <td>44.44</td>\n",
       "      <td>43.88</td>\n",
       "      <td>44.24</td>\n",
       "      <td>44.24</td>\n",
       "      <td>0.64</td>\n",
       "      <td>22.87</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>002218</td>\n",
       "      <td>拓日新能</td>\n",
       "      <td>6.04</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>447111</td>\n",
       "      <td>2.680567e+08</td>\n",
       "      <td>1.65</td>\n",
       "      <td>6.05</td>\n",
       "      <td>5.95</td>\n",
       "      <td>6.05</td>\n",
       "      <td>6.07</td>\n",
       "      <td>3.22</td>\n",
       "      <td>60.24</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>301278</td>\n",
       "      <td>快可电子</td>\n",
       "      <td>97.36</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>6003</td>\n",
       "      <td>5.817504e+07</td>\n",
       "      <td>1.89</td>\n",
       "      <td>98.05</td>\n",
       "      <td>96.20</td>\n",
       "      <td>98.01</td>\n",
       "      <td>98.01</td>\n",
       "      <td>3.75</td>\n",
       "      <td>53.56</td>\n",
       "      <td>6.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>600537</td>\n",
       "      <td>亿晶光电</td>\n",
       "      <td>8.49</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>233097</td>\n",
       "      <td>1.982891e+08</td>\n",
       "      <td>1.75</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.58</td>\n",
       "      <td>1.98</td>\n",
       "      <td>158.25</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>603396</td>\n",
       "      <td>金辰股份</td>\n",
       "      <td>79.45</td>\n",
       "      <td>-1.13</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>16373</td>\n",
       "      <td>1.299424e+08</td>\n",
       "      <td>2.09</td>\n",
       "      <td>80.78</td>\n",
       "      <td>79.10</td>\n",
       "      <td>79.98</td>\n",
       "      <td>80.36</td>\n",
       "      <td>1.41</td>\n",
       "      <td>125.91</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>002623</td>\n",
       "      <td>亚玛顿</td>\n",
       "      <td>35.39</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>84135</td>\n",
       "      <td>2.955642e+08</td>\n",
       "      <td>3.44</td>\n",
       "      <td>35.93</td>\n",
       "      <td>34.70</td>\n",
       "      <td>35.91</td>\n",
       "      <td>35.80</td>\n",
       "      <td>4.24</td>\n",
       "      <td>89.29</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>688560</td>\n",
       "      <td>明冠新材</td>\n",
       "      <td>37.43</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>96227</td>\n",
       "      <td>3.609724e+08</td>\n",
       "      <td>2.80</td>\n",
       "      <td>38.10</td>\n",
       "      <td>37.04</td>\n",
       "      <td>37.39</td>\n",
       "      <td>37.88</td>\n",
       "      <td>10.65</td>\n",
       "      <td>72.78</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>300842</td>\n",
       "      <td>帝科股份</td>\n",
       "      <td>47.61</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>17698</td>\n",
       "      <td>8.423682e+07</td>\n",
       "      <td>2.43</td>\n",
       "      <td>48.36</td>\n",
       "      <td>47.19</td>\n",
       "      <td>48.20</td>\n",
       "      <td>48.24</td>\n",
       "      <td>2.55</td>\n",
       "      <td>252.36</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>002309</td>\n",
       "      <td>ST中利</td>\n",
       "      <td>5.08</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>128571</td>\n",
       "      <td>6.517071e+07</td>\n",
       "      <td>2.14</td>\n",
       "      <td>5.12</td>\n",
       "      <td>5.01</td>\n",
       "      <td>5.12</td>\n",
       "      <td>5.15</td>\n",
       "      <td>1.72</td>\n",
       "      <td>-16.76</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>300316</td>\n",
       "      <td>晶盛机电</td>\n",
       "      <td>67.62</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>51921</td>\n",
       "      <td>3.507813e+08</td>\n",
       "      <td>2.51</td>\n",
       "      <td>68.78</td>\n",
       "      <td>67.06</td>\n",
       "      <td>68.68</td>\n",
       "      <td>68.60</td>\n",
       "      <td>0.42</td>\n",
       "      <td>33.04</td>\n",
       "      <td>9.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>301168</td>\n",
       "      <td>通灵股份</td>\n",
       "      <td>66.55</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>16747</td>\n",
       "      <td>1.118620e+08</td>\n",
       "      <td>4.86</td>\n",
       "      <td>69.19</td>\n",
       "      <td>65.90</td>\n",
       "      <td>68.00</td>\n",
       "      <td>67.70</td>\n",
       "      <td>3.37</td>\n",
       "      <td>63.49</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>603806</td>\n",
       "      <td>福斯特</td>\n",
       "      <td>67.50</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>64427</td>\n",
       "      <td>4.333333e+08</td>\n",
       "      <td>2.94</td>\n",
       "      <td>68.70</td>\n",
       "      <td>66.68</td>\n",
       "      <td>68.69</td>\n",
       "      <td>68.69</td>\n",
       "      <td>0.48</td>\n",
       "      <td>43.96</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>688599</td>\n",
       "      <td>天合光能</td>\n",
       "      <td>60.61</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>112111</td>\n",
       "      <td>6.814908e+08</td>\n",
       "      <td>2.69</td>\n",
       "      <td>61.88</td>\n",
       "      <td>60.22</td>\n",
       "      <td>61.80</td>\n",
       "      <td>61.70</td>\n",
       "      <td>0.84</td>\n",
       "      <td>35.49</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>603212</td>\n",
       "      <td>赛伍技术</td>\n",
       "      <td>28.34</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>118563</td>\n",
       "      <td>3.348826e+08</td>\n",
       "      <td>2.56</td>\n",
       "      <td>28.74</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.74</td>\n",
       "      <td>28.85</td>\n",
       "      <td>4.05</td>\n",
       "      <td>40.96</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>688556</td>\n",
       "      <td>高测股份</td>\n",
       "      <td>72.15</td>\n",
       "      <td>-2.37</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>32715</td>\n",
       "      <td>2.371296e+08</td>\n",
       "      <td>2.96</td>\n",
       "      <td>74.00</td>\n",
       "      <td>71.81</td>\n",
       "      <td>73.70</td>\n",
       "      <td>73.90</td>\n",
       "      <td>1.93</td>\n",
       "      <td>20.85</td>\n",
       "      <td>7.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>300763</td>\n",
       "      <td>锦浪科技</td>\n",
       "      <td>155.00</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-3.78</td>\n",
       "      <td>36921</td>\n",
       "      <td>5.755402e+08</td>\n",
       "      <td>2.18</td>\n",
       "      <td>158.30</td>\n",
       "      <td>154.84</td>\n",
       "      <td>158.30</td>\n",
       "      <td>158.78</td>\n",
       "      <td>1.23</td>\n",
       "      <td>65.56</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>300827</td>\n",
       "      <td>上能电气</td>\n",
       "      <td>67.97</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>62064</td>\n",
       "      <td>4.209149e+08</td>\n",
       "      <td>4.79</td>\n",
       "      <td>70.30</td>\n",
       "      <td>66.96</td>\n",
       "      <td>69.80</td>\n",
       "      <td>69.71</td>\n",
       "      <td>5.76</td>\n",
       "      <td>266.74</td>\n",
       "      <td>17.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>688348</td>\n",
       "      <td>昱能科技</td>\n",
       "      <td>445.58</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-11.92</td>\n",
       "      <td>10738</td>\n",
       "      <td>4.803970e+08</td>\n",
       "      <td>3.57</td>\n",
       "      <td>458.00</td>\n",
       "      <td>441.66</td>\n",
       "      <td>457.50</td>\n",
       "      <td>457.50</td>\n",
       "      <td>5.41</td>\n",
       "      <td>103.09</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>688598</td>\n",
       "      <td>金博股份</td>\n",
       "      <td>209.81</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-6.45</td>\n",
       "      <td>11398</td>\n",
       "      <td>2.401117e+08</td>\n",
       "      <td>2.88</td>\n",
       "      <td>215.10</td>\n",
       "      <td>208.88</td>\n",
       "      <td>215.03</td>\n",
       "      <td>216.26</td>\n",
       "      <td>1.44</td>\n",
       "      <td>35.81</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>600732</td>\n",
       "      <td>爱旭股份</td>\n",
       "      <td>35.44</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>275804</td>\n",
       "      <td>9.739928e+08</td>\n",
       "      <td>3.14</td>\n",
       "      <td>36.25</td>\n",
       "      <td>35.10</td>\n",
       "      <td>36.25</td>\n",
       "      <td>36.59</td>\n",
       "      <td>2.99</td>\n",
       "      <td>24.90</td>\n",
       "      <td>5.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>688408</td>\n",
       "      <td>中信博</td>\n",
       "      <td>102.64</td>\n",
       "      <td>-3.17</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>25073</td>\n",
       "      <td>2.579798e+08</td>\n",
       "      <td>5.58</td>\n",
       "      <td>107.10</td>\n",
       "      <td>101.18</td>\n",
       "      <td>106.20</td>\n",
       "      <td>106.00</td>\n",
       "      <td>3.43</td>\n",
       "      <td>318.40</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>688223</td>\n",
       "      <td>晶科能源</td>\n",
       "      <td>15.04</td>\n",
       "      <td>-3.28</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>697162</td>\n",
       "      <td>1.060185e+09</td>\n",
       "      <td>4.05</td>\n",
       "      <td>15.64</td>\n",
       "      <td>15.01</td>\n",
       "      <td>15.60</td>\n",
       "      <td>15.55</td>\n",
       "      <td>3.56</td>\n",
       "      <td>51.03</td>\n",
       "      <td>5.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>300274</td>\n",
       "      <td>阳光电源</td>\n",
       "      <td>115.40</td>\n",
       "      <td>-3.51</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>225769</td>\n",
       "      <td>2.606848e+09</td>\n",
       "      <td>4.70</td>\n",
       "      <td>119.80</td>\n",
       "      <td>114.18</td>\n",
       "      <td>119.09</td>\n",
       "      <td>119.60</td>\n",
       "      <td>2.00</td>\n",
       "      <td>62.38</td>\n",
       "      <td>10.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>002459</td>\n",
       "      <td>晶澳科技</td>\n",
       "      <td>58.85</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>196903</td>\n",
       "      <td>1.162909e+09</td>\n",
       "      <td>4.43</td>\n",
       "      <td>61.21</td>\n",
       "      <td>58.50</td>\n",
       "      <td>61.15</td>\n",
       "      <td>61.12</td>\n",
       "      <td>0.84</td>\n",
       "      <td>31.60</td>\n",
       "      <td>5.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>002865</td>\n",
       "      <td>钧达股份</td>\n",
       "      <td>181.85</td>\n",
       "      <td>-4.33</td>\n",
       "      <td>-8.24</td>\n",
       "      <td>58020</td>\n",
       "      <td>1.061024e+09</td>\n",
       "      <td>4.80</td>\n",
       "      <td>189.20</td>\n",
       "      <td>180.08</td>\n",
       "      <td>189.14</td>\n",
       "      <td>190.09</td>\n",
       "      <td>4.20</td>\n",
       "      <td>47.04</td>\n",
       "      <td>36.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>688680</td>\n",
       "      <td>海优新材</td>\n",
       "      <td>193.00</td>\n",
       "      <td>-4.46</td>\n",
       "      <td>-9.00</td>\n",
       "      <td>14907</td>\n",
       "      <td>2.891351e+08</td>\n",
       "      <td>3.42</td>\n",
       "      <td>199.01</td>\n",
       "      <td>192.10</td>\n",
       "      <td>199.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>2.86</td>\n",
       "      <td>324.91</td>\n",
       "      <td>6.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>300118</td>\n",
       "      <td>东方日升</td>\n",
       "      <td>29.94</td>\n",
       "      <td>-4.65</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>438791</td>\n",
       "      <td>1.322037e+09</td>\n",
       "      <td>4.62</td>\n",
       "      <td>31.26</td>\n",
       "      <td>29.81</td>\n",
       "      <td>31.15</td>\n",
       "      <td>31.40</td>\n",
       "      <td>6.34</td>\n",
       "      <td>34.25</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>688390</td>\n",
       "      <td>固德威</td>\n",
       "      <td>367.38</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-21.11</td>\n",
       "      <td>20305</td>\n",
       "      <td>7.526198e+08</td>\n",
       "      <td>4.89</td>\n",
       "      <td>385.00</td>\n",
       "      <td>366.00</td>\n",
       "      <td>384.12</td>\n",
       "      <td>388.49</td>\n",
       "      <td>2.51</td>\n",
       "      <td>69.39</td>\n",
       "      <td>20.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>001269</td>\n",
       "      <td>欧晶科技</td>\n",
       "      <td>112.60</td>\n",
       "      <td>-5.81</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>70601</td>\n",
       "      <td>7.841430e+08</td>\n",
       "      <td>10.70</td>\n",
       "      <td>120.39</td>\n",
       "      <td>107.60</td>\n",
       "      <td>119.11</td>\n",
       "      <td>119.55</td>\n",
       "      <td>20.55</td>\n",
       "      <td>75.06</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>688032</td>\n",
       "      <td>禾迈股份</td>\n",
       "      <td>796.01</td>\n",
       "      <td>-6.46</td>\n",
       "      <td>-54.99</td>\n",
       "      <td>9741</td>\n",
       "      <td>7.950700e+08</td>\n",
       "      <td>6.87</td>\n",
       "      <td>852.50</td>\n",
       "      <td>794.00</td>\n",
       "      <td>843.50</td>\n",
       "      <td>851.00</td>\n",
       "      <td>3.49</td>\n",
       "      <td>83.76</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>834770</td>\n",
       "      <td>艾能聚</td>\n",
       "      <td>7.98</td>\n",
       "      <td>-9.11</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>159113</td>\n",
       "      <td>1.295688e+08</td>\n",
       "      <td>7.63</td>\n",
       "      <td>8.53</td>\n",
       "      <td>7.86</td>\n",
       "      <td>8.30</td>\n",
       "      <td>8.78</td>\n",
       "      <td>25.74</td>\n",
       "      <td>16.42</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    序号      代码     名称     最新价   涨跌幅    涨跌额     成交量           成交额     振幅  \\\n",
       "0    1  603105   芯能科技   17.72  1.43   0.25  138796  2.432346e+08   2.58   \n",
       "1    2  300393   中来股份   17.18  1.12   0.19  270884  4.590574e+08   2.83   \n",
       "2    3  002129  TCL中环   44.12  1.08   0.47  258274  1.135493e+09   1.95   \n",
       "3    4  835985   海泰新能    8.94  1.02   0.09   12233  1.082701e+07   2.71   \n",
       "4    5  301046   能辉科技   40.70  0.74   0.30   29262  1.179232e+08   2.20   \n",
       "5    6  300029   ST天龙    8.29  0.48   0.04   19548  1.617148e+07   1.70   \n",
       "6    7  300317   珈伟新能    6.25  0.48   0.03  228560  1.415638e+08   1.93   \n",
       "7    8  600207   安彩高科    6.72  0.45   0.03   64537  4.306403e+07   1.64   \n",
       "8    9  002610   爱康科技    2.85  0.35   0.01  431486  1.225051e+08   1.41   \n",
       "9   10  603628   清源股份   16.75  0.30   0.05   20764  3.453946e+07   1.26   \n",
       "10  11  688303   大全能源   48.07  0.29   0.14   72410  3.477293e+08   0.86   \n",
       "11  12  603185   上机数控  111.27  0.18   0.20   28052  3.115888e+08   1.46   \n",
       "12  13  002617   露笑科技    8.92  0.11   0.01  205556  1.829574e+08   0.67   \n",
       "13  14  688516    奥特维  196.10  0.10   0.20    6425  1.248727e+08   2.14   \n",
       "14  15  600151   航天机电   10.00  0.00   0.00   78211  7.790840e+07   1.10   \n",
       "15  16  002506   协鑫集成    3.17  0.00   0.00  427841  1.349946e+08   1.26   \n",
       "16  17  600438   通威股份   41.88 -0.05  -0.02  425909  1.783328e+09   1.41   \n",
       "17  18  601012   隆基绿能   44.14 -0.23  -0.10  481804  2.126099e+09   1.27   \n",
       "18  19  002218   拓日新能    6.04 -0.49  -0.03  447111  2.680567e+08   1.65   \n",
       "19  20  301278   快可电子   97.36 -0.66  -0.65    6003  5.817504e+07   1.89   \n",
       "20  21  600537   亿晶光电    8.49 -1.05  -0.09  233097  1.982891e+08   1.75   \n",
       "21  22  603396   金辰股份   79.45 -1.13  -0.91   16373  1.299424e+08   2.09   \n",
       "22  23  002623    亚玛顿   35.39 -1.15  -0.41   84135  2.955642e+08   3.44   \n",
       "23  24  688560   明冠新材   37.43 -1.19  -0.45   96227  3.609724e+08   2.80   \n",
       "24  25  300842   帝科股份   47.61 -1.31  -0.63   17698  8.423682e+07   2.43   \n",
       "25  26  002309   ST中利    5.08 -1.36  -0.07  128571  6.517071e+07   2.14   \n",
       "26  27  300316   晶盛机电   67.62 -1.43  -0.98   51921  3.507813e+08   2.51   \n",
       "27  28  301168   通灵股份   66.55 -1.70  -1.15   16747  1.118620e+08   4.86   \n",
       "28  29  603806    福斯特   67.50 -1.73  -1.19   64427  4.333333e+08   2.94   \n",
       "29  30  688599   天合光能   60.61 -1.77  -1.09  112111  6.814908e+08   2.69   \n",
       "30  31  603212   赛伍技术   28.34 -1.77  -0.51  118563  3.348826e+08   2.56   \n",
       "31  32  688556   高测股份   72.15 -2.37  -1.75   32715  2.371296e+08   2.96   \n",
       "32  33  300763   锦浪科技  155.00 -2.38  -3.78   36921  5.755402e+08   2.18   \n",
       "33  34  300827   上能电气   67.97 -2.50  -1.74   62064  4.209149e+08   4.79   \n",
       "34  35  688348   昱能科技  445.58 -2.61 -11.92   10738  4.803970e+08   3.57   \n",
       "35  36  688598   金博股份  209.81 -2.98  -6.45   11398  2.401117e+08   2.88   \n",
       "36  37  600732   爱旭股份   35.44 -3.14  -1.15  275804  9.739928e+08   3.14   \n",
       "37  38  688408    中信博  102.64 -3.17  -3.36   25073  2.579798e+08   5.58   \n",
       "38  39  688223   晶科能源   15.04 -3.28  -0.51  697162  1.060185e+09   4.05   \n",
       "39  40  300274   阳光电源  115.40 -3.51  -4.20  225769  2.606848e+09   4.70   \n",
       "40  41  002459   晶澳科技   58.85 -3.71  -2.27  196903  1.162909e+09   4.43   \n",
       "41  42  002865   钧达股份  181.85 -4.33  -8.24   58020  1.061024e+09   4.80   \n",
       "42  43  688680   海优新材  193.00 -4.46  -9.00   14907  2.891351e+08   3.42   \n",
       "43  44  300118   东方日升   29.94 -4.65  -1.46  438791  1.322037e+09   4.62   \n",
       "44  45  688390    固德威  367.38 -5.43 -21.11   20305  7.526198e+08   4.89   \n",
       "45  46  001269   欧晶科技  112.60 -5.81  -6.95   70601  7.841430e+08  10.70   \n",
       "46  47  688032   禾迈股份  796.01 -6.46 -54.99    9741  7.950700e+08   6.87   \n",
       "47  48  834770    艾能聚    7.98 -9.11  -0.80  159113  1.295688e+08   7.63   \n",
       "\n",
       "        最高      最低      今开      昨收    换手率   市盈率-动态    市净率  \n",
       "0    17.75   17.30   17.50   17.47   2.78    41.32   5.11  \n",
       "1    17.20   16.72   16.99   16.99   2.84    40.92   4.93  \n",
       "2    44.38   43.53   43.86   43.65   0.80    21.38   3.94  \n",
       "3     8.95    8.71    8.89    8.85   0.91    29.37   2.19  \n",
       "4    40.80   39.91   40.46   40.40   4.96   311.92   8.05  \n",
       "5     8.33    8.19    8.25    8.25   0.97    35.22  32.47  \n",
       "6     6.25    6.13    6.20    6.22   2.95    41.43   2.82  \n",
       "7     6.73    6.62    6.70    6.69   0.75    53.12   2.32  \n",
       "8     2.86    2.82    2.85    2.84   0.98   -35.17   3.84  \n",
       "9    16.75   16.54   16.67   16.70   0.76    50.36   4.29  \n",
       "10   48.17   47.76   47.95   47.93   1.43     5.37   2.26  \n",
       "11  111.80  110.18  111.00  111.07   0.69    12.11   4.50  \n",
       "12    8.93    8.87    8.92    8.91   1.27  -217.24   2.80  \n",
       "13  196.78  192.59  195.99  195.90   0.85    43.40  11.88  \n",
       "14   10.02    9.91    9.95   10.00   0.55 -1160.06   2.68  \n",
       "15    3.18    3.14    3.15    3.17   0.73  -137.44   8.63  \n",
       "16   42.24   41.65   41.95   41.90   0.95     6.51   3.44  \n",
       "17   44.44   43.88   44.24   44.24   0.64    22.87   5.80  \n",
       "18    6.05    5.95    6.05    6.07   3.22    60.24   2.00  \n",
       "19   98.05   96.20   98.01   98.01   3.75    53.56   6.39  \n",
       "20    8.60    8.45    8.54    8.58   1.98   158.25   4.24  \n",
       "21   80.78   79.10   79.98   80.36   1.41   125.91   6.38  \n",
       "22   35.93   34.70   35.91   35.80   4.24    89.29   2.15  \n",
       "23   38.10   37.04   37.39   37.88  10.65    72.78   2.40  \n",
       "24   48.36   47.19   48.20   48.24   2.55   252.36   5.01  \n",
       "25    5.12    5.01    5.12    5.15   1.72   -16.76   2.88  \n",
       "26   68.78   67.06   68.68   68.60   0.42    33.04   9.01  \n",
       "27   69.19   65.90   68.00   67.70   3.37    63.49   4.15  \n",
       "28   68.70   66.68   68.69   68.69   0.48    43.96   6.73  \n",
       "29   61.88   60.22   61.80   61.70   0.84    35.49   5.00  \n",
       "30   28.74   28.00   28.74   28.85   4.05    40.96   4.16  \n",
       "31   74.00   71.81   73.70   73.90   1.93    20.85   7.96  \n",
       "32  158.30  154.84  158.30  158.78   1.23    65.56   9.05  \n",
       "33   70.30   66.96   69.80   69.71   5.76   266.74  17.51  \n",
       "34  458.00  441.66  457.50  457.50   5.41   103.09   9.67  \n",
       "35  215.10  208.88  215.03  216.26   1.44    35.81   3.30  \n",
       "36   36.25   35.10   36.25   36.59   2.99    24.90   5.67  \n",
       "37  107.10  101.18  106.20  106.00   3.43   318.40   5.58  \n",
       "38   15.64   15.01   15.60   15.55   3.56    51.03   5.63  \n",
       "39  119.80  114.18  119.09  119.60   2.00    62.38  10.03  \n",
       "40   61.21   58.50   61.15   61.12   0.84    31.60   5.51  \n",
       "41  189.20  180.08  189.14  190.09   4.20    47.04  36.25  \n",
       "42  199.01  192.10  199.00  202.00   2.86   324.91   6.54  \n",
       "43   31.26   29.81   31.15   31.40   6.34    34.25   2.41  \n",
       "44  385.00  366.00  384.12  388.49   2.51    69.39  20.02  \n",
       "45  120.39  107.60  119.11  119.55  20.55    75.06  14.63  \n",
       "46  852.50  794.00  843.50  851.00   3.49    83.76   6.96  \n",
       "47    8.53    7.86    8.30    8.78  25.74    16.42   1.96  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_board_industry_cons_em_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "belongs={}\n",
    "for i in holding_ordered:\n",
    "    for k,v in bk_result.items():\n",
    "        if i[0] in v:\n",
    "            belongs[i[0]]=k\n",
    "            break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'贵州茅台': '酿酒行业',\n",
       " '兴业银行': '银行',\n",
       " '伊利股份': '食品饮料',\n",
       " '紫光国微': '半导体',\n",
       " '立讯精密': '消费电子',\n",
       " '天顺风能': '风电设备',\n",
       " '天合光能': '光伏设备',\n",
       " '平安银行': '银行',\n",
       " '中国巨石': '玻璃玻纤',\n",
       " '福耀玻璃': '玻璃玻纤',\n",
       " '正泰电器': '电网设备',\n",
       " '恒力石化': '化纤行业',\n",
       " '古井贡酒': '酿酒行业',\n",
       " '神火股份': '有色金属',\n",
       " '紫金矿业': '贵金属',\n",
       " '兖矿能源': '煤炭行业',\n",
       " '潞安环能': '煤炭行业',\n",
       " '山煤国际': '煤炭行业',\n",
       " '中船防务': '船舶制造',\n",
       " '山西焦煤': '煤炭行业',\n",
       " '纳思达': '计算机设备',\n",
       " '南都电源': '电池',\n",
       " '比亚迪': '汽车整车',\n",
       " '宝信软件': '互联网服务',\n",
       " '航天宏图': '互联网服务',\n",
       " '永兴材料': '钢铁行业',\n",
       " '宁德时代': '电池',\n",
       " '华电国际': '电力行业',\n",
       " '锦江酒店': '旅游酒店',\n",
       " '京沪高铁': '物流行业',\n",
       " '老白干酒': '酿酒行业',\n",
       " '南方航空': '航空机场',\n",
       " '海尔智家': '家电行业',\n",
       " '红日药业': '中药',\n",
       " '海油工程': '采掘行业',\n",
       " '中国人寿': '保险',\n",
       " '陕西煤业': '煤炭行业',\n",
       " '上海电力': '电力行业',\n",
       " '中国神华': '煤炭行业',\n",
       " '中煤能源': '煤炭行业',\n",
       " '广发证券': '证券',\n",
       " '招商证券': '证券',\n",
       " '中信证券': '证券',\n",
       " '晋控煤业': '煤炭行业',\n",
       " '淮北矿业': '煤炭行业',\n",
       " '三峡能源': '电力行业',\n",
       " '广汇能源': '石油行业',\n",
       " '阳光电源': '光伏设备',\n",
       " '宁波银行': '银行',\n",
       " '三花智控': '家电行业',\n",
       " '卫星化学': '化学原料',\n",
       " '恒立液压': '工程机械',\n",
       " '邮储银行': '银行',\n",
       " '万华化学': '化学制品',\n",
       " '中国建筑': '工程建设',\n",
       " '保利发展': '房地产开发',\n",
       " '南京银行': '银行',\n",
       " '招商蛇口': '房地产开发',\n",
       " '光大银行': '银行',\n",
       " '金地集团': '房地产开发',\n",
       " '成都银行': '银行',\n",
       " '杭州银行': '银行',\n",
       " '江苏银行': '银行',\n",
       " '通威股份': '光伏设备',\n",
       " '中科创达': '互联网服务',\n",
       " '智飞生物': '生物制品',\n",
       " '华东医药': '化学制药',\n",
       " '华鲁恒升': '化学原料',\n",
       " '鱼跃医疗': '医疗器械',\n",
       " '中国中免': '旅游酒店',\n",
       " '先导智能': '电池',\n",
       " '振华科技': '电子元件',\n",
       " '隆基绿能': '光伏设备',\n",
       " '山西汾酒': '酿酒行业',\n",
       " '泸州老窖': '酿酒行业',\n",
       " '华恒生物': '化学制品',\n",
       " '圆通速递': '物流行业',\n",
       " '福斯特': '光伏设备',\n",
       " '亿联网络': '通信设备',\n",
       " '华熙生物': '美容护理',\n",
       " '爱尔眼科': '医疗服务',\n",
       " '贝泰妮': '美容护理',\n",
       " '通策医疗': '医疗服务',\n",
       " '爱美客': '美容护理',\n",
       " '安琪酵母': '食品饮料',\n",
       " '海天味业': '食品饮料',\n",
       " '安井食品': '食品饮料',\n",
       " '珀莱雅': '美容护理'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "belongs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_={}\n",
    "result__={}\n",
    "for k,v in belongs.items():\n",
    "    result_.setdefault(v,0)\n",
    "    result_[v]+=1\n",
    "    if v not in result__:\n",
    "        result__[v]=[]\n",
    "    result__[v].append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "酿酒行业 ['贵州茅台', '古井贡酒', '老白干酒', '山西汾酒', '泸州老窖']\n",
      "银行 ['兴业银行', '平安银行', '宁波银行', '邮储银行', '南京银行', '光大银行', '成都银行', '杭州银行', '江苏银行']\n",
      "食品饮料 ['伊利股份', '安琪酵母', '海天味业', '安井食品']\n",
      "光伏设备 ['天合光能', '阳光电源', '通威股份', '隆基绿能', '福斯特']\n",
      "煤炭行业 ['兖矿能源', '潞安环能', '山煤国际', '山西焦煤', '陕西煤业', '中国神华', '中煤能源', '晋控煤业', '淮北矿业']\n",
      "美容护理 ['华熙生物', '贝泰妮', '爱美客', '珀莱雅']\n"
     ]
    }
   ],
   "source": [
    "for name,item in result__.items():\n",
    "    if len(item)>3:\n",
    "        print(name,item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "industry_dict={}\n",
    "for code,df in holding_dict.items():\n",
    "    h_list = df['股票名称'].tolist()\n",
    "    for i in h_list:\n",
    "        industry = belongs.get(i)\n",
    "        industry_dict.setdefault(industry,0)\n",
    "        industry_dict[industry]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'酿酒行业': 10,\n",
       " '银行': 10,\n",
       " '风电设备': 1,\n",
       " '光伏设备': 5,\n",
       " '玻璃玻纤': 2,\n",
       " '电网设备': 1,\n",
       " '化纤行业': 1,\n",
       " '食品饮料': 5,\n",
       " '有色金属': 1,\n",
       " '贵金属': 1,\n",
       " '煤炭行业': 9,\n",
       " None: 3,\n",
       " '船舶制造': 1,\n",
       " '计算机设备': 1,\n",
       " '电池': 3,\n",
       " '汽车整车': 1,\n",
       " '互联网服务': 3,\n",
       " '半导体': 2,\n",
       " '钢铁行业': 1,\n",
       " '电力行业': 3,\n",
       " '旅游酒店': 2,\n",
       " '物流行业': 2,\n",
       " '航空机场': 1,\n",
       " '家电行业': 2,\n",
       " '中药': 1,\n",
       " '采掘行业': 1,\n",
       " '保险': 1,\n",
       " '证券': 3,\n",
       " '石油行业': 1,\n",
       " '化学原料': 2,\n",
       " '工程机械': 1,\n",
       " '消费电子': 2,\n",
       " '化学制品': 2,\n",
       " '工程建设': 1,\n",
       " '房地产开发': 3,\n",
       " '生物制品': 1,\n",
       " '化学制药': 1,\n",
       " '医疗器械': 1,\n",
       " '电子元件': 1,\n",
       " '通信设备': 1,\n",
       " '美容护理': 4,\n",
       " '医疗服务': 2}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "酿酒行业 10\n",
      "银行 10\n",
      "煤炭行业 9\n",
      "光伏设备 5\n",
      "食品饮料 5\n",
      "美容护理 4\n",
      "None 3\n",
      "电池 3\n",
      "互联网服务 3\n",
      "电力行业 3\n",
      "证券 3\n",
      "房地产开发 3\n",
      "玻璃玻纤 2\n",
      "半导体 2\n",
      "旅游酒店 2\n",
      "物流行业 2\n",
      "家电行业 2\n",
      "化学原料 2\n",
      "消费电子 2\n",
      "化学制品 2\n",
      "医疗服务 2\n",
      "风电设备 1\n",
      "电网设备 1\n",
      "化纤行业 1\n",
      "有色金属 1\n",
      "贵金属 1\n",
      "船舶制造 1\n",
      "计算机设备 1\n",
      "汽车整车 1\n",
      "钢铁行业 1\n",
      "航空机场 1\n",
      "中药 1\n",
      "采掘行业 1\n",
      "保险 1\n",
      "石油行业 1\n",
      "工程机械 1\n",
      "工程建设 1\n",
      "生物制品 1\n",
      "化学制药 1\n",
      "医疗器械 1\n",
      "电子元件 1\n",
      "通信设备 1\n"
     ]
    }
   ],
   "source": [
    "ret = list(sorted(industry_dict.items(),key=lambda x:x[1],reverse=True))\n",
    "for name,count in ret:\n",
    "    print(name,count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'酿酒行业': ['贵州茅台', '古井贡酒', '老白干酒', '山西汾酒', '泸州老窖'],\n",
       " '银行': ['兴业银行',\n",
       "  '平安银行',\n",
       "  '宁波银行',\n",
       "  '邮储银行',\n",
       "  '南京银行',\n",
       "  '光大银行',\n",
       "  '成都银行',\n",
       "  '杭州银行',\n",
       "  '江苏银行'],\n",
       " '食品饮料': ['伊利股份', '安琪酵母', '海天味业', '安井食品'],\n",
       " '半导体': ['紫光国微'],\n",
       " '消费电子': ['立讯精密'],\n",
       " '风电设备': ['天顺风能'],\n",
       " '光伏设备': ['天合光能', '阳光电源', '通威股份', '隆基绿能', '福斯特'],\n",
       " '玻璃玻纤': ['中国巨石', '福耀玻璃'],\n",
       " '电网设备': ['正泰电器'],\n",
       " '化纤行业': ['恒力石化'],\n",
       " '有色金属': ['神火股份'],\n",
       " '贵金属': ['紫金矿业'],\n",
       " '煤炭行业': ['兖矿能源',\n",
       "  '潞安环能',\n",
       "  '山煤国际',\n",
       "  '山西焦煤',\n",
       "  '陕西煤业',\n",
       "  '中国神华',\n",
       "  '中煤能源',\n",
       "  '晋控煤业',\n",
       "  '淮北矿业'],\n",
       " '船舶制造': ['中船防务'],\n",
       " '计算机设备': ['纳思达'],\n",
       " '电池': ['南都电源', '宁德时代', '先导智能'],\n",
       " '汽车整车': ['比亚迪'],\n",
       " '互联网服务': ['宝信软件', '航天宏图', '中科创达'],\n",
       " '钢铁行业': ['永兴材料'],\n",
       " '电力行业': ['华电国际', '上海电力', '三峡能源'],\n",
       " '旅游酒店': ['锦江酒店', '中国中免'],\n",
       " '物流行业': ['京沪高铁', '圆通速递'],\n",
       " '航空机场': ['南方航空'],\n",
       " '家电行业': ['海尔智家', '三花智控'],\n",
       " '中药': ['红日药业'],\n",
       " '采掘行业': ['海油工程'],\n",
       " '保险': ['中国人寿'],\n",
       " '证券': ['广发证券', '招商证券', '中信证券'],\n",
       " '石油行业': ['广汇能源'],\n",
       " '化学原料': ['卫星化学', '华鲁恒升'],\n",
       " '工程机械': ['恒立液压'],\n",
       " '化学制品': ['万华化学', '华恒生物'],\n",
       " '工程建设': ['中国建筑'],\n",
       " '房地产开发': ['保利发展', '招商蛇口', '金地集团'],\n",
       " '生物制品': ['智飞生物'],\n",
       " '化学制药': ['华东医药'],\n",
       " '医疗器械': ['鱼跃医疗'],\n",
       " '电子元件': ['振华科技'],\n",
       " '通信设备': ['亿联网络'],\n",
       " '美容护理': ['华熙生物', '贝泰妮', '爱美客', '珀莱雅'],\n",
       " '医疗服务': ['爱尔眼科', '通策医疗']}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'酿酒行业': 5,\n",
       " '银行': 9,\n",
       " '食品饮料': 4,\n",
       " '半导体': 1,\n",
       " '消费电子': 1,\n",
       " '风电设备': 1,\n",
       " '光伏设备': 5,\n",
       " '玻璃玻纤': 2,\n",
       " '电网设备': 1,\n",
       " '化纤行业': 1,\n",
       " '有色金属': 1,\n",
       " '贵金属': 1,\n",
       " '煤炭行业': 9,\n",
       " '船舶制造': 1,\n",
       " '计算机设备': 1,\n",
       " '电池': 3,\n",
       " '汽车整车': 1,\n",
       " '互联网服务': 3,\n",
       " '钢铁行业': 1,\n",
       " '电力行业': 3,\n",
       " '旅游酒店': 2,\n",
       " '物流行业': 2,\n",
       " '航空机场': 1,\n",
       " '家电行业': 2,\n",
       " '中药': 1,\n",
       " '采掘行业': 1,\n",
       " '保险': 1,\n",
       " '证券': 3,\n",
       " '石油行业': 1,\n",
       " '化学原料': 2,\n",
       " '工程机械': 1,\n",
       " '化学制品': 2,\n",
       " '工程建设': 1,\n",
       " '房地产开发': 3,\n",
       " '生物制品': 1,\n",
       " '化学制药': 1,\n",
       " '医疗器械': 1,\n",
       " '电子元件': 1,\n",
       " '通信设备': 1,\n",
       " '美容护理': 4,\n",
       " '医疗服务': 2}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_industry = sorted(result_.items(),key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "银行 9\n",
      "['兴业银行', '平安银行', '宁波银行', '邮储银行', '南京银行', '光大银行', '成都银行', '杭州银行', '江苏银行']\n",
      "煤炭行业 9\n",
      "['兖矿能源', '潞安环能', '山煤国际', '山西焦煤', '陕西煤业', '中国神华', '中煤能源', '晋控煤业', '淮北矿业']\n",
      "酿酒行业 5\n",
      "['贵州茅台', '古井贡酒', '老白干酒', '山西汾酒', '泸州老窖']\n",
      "光伏设备 5\n",
      "['天合光能', '阳光电源', '通威股份', '隆基绿能', '福斯特']\n",
      "食品饮料 4\n",
      "['伊利股份', '安琪酵母', '海天味业', '安井食品']\n",
      "美容护理 4\n",
      "['华熙生物', '贝泰妮', '爱美客', '珀莱雅']\n",
      "电池 3\n",
      "['南都电源', '宁德时代', '先导智能']\n",
      "互联网服务 3\n",
      "['宝信软件', '航天宏图', '中科创达']\n",
      "电力行业 3\n",
      "['华电国际', '上海电力', '三峡能源']\n",
      "证券 3\n",
      "['广发证券', '招商证券', '中信证券']\n",
      "房地产开发 3\n",
      "['保利发展', '招商蛇口', '金地集团']\n",
      "玻璃玻纤 2\n",
      "['中国巨石', '福耀玻璃']\n",
      "旅游酒店 2\n",
      "['锦江酒店', '中国中免']\n",
      "物流行业 2\n",
      "['京沪高铁', '圆通速递']\n",
      "家电行业 2\n",
      "['海尔智家', '三花智控']\n",
      "化学原料 2\n",
      "['卫星化学', '华鲁恒升']\n",
      "化学制品 2\n",
      "['万华化学', '华恒生物']\n",
      "医疗服务 2\n",
      "['爱尔眼科', '通策医疗']\n",
      "半导体 1\n",
      "['紫光国微']\n",
      "消费电子 1\n",
      "['立讯精密']\n",
      "风电设备 1\n",
      "['天顺风能']\n",
      "电网设备 1\n",
      "['正泰电器']\n",
      "化纤行业 1\n",
      "['恒力石化']\n",
      "有色金属 1\n",
      "['神火股份']\n",
      "贵金属 1\n",
      "['紫金矿业']\n",
      "船舶制造 1\n",
      "['中船防务']\n",
      "计算机设备 1\n",
      "['纳思达']\n",
      "汽车整车 1\n",
      "['比亚迪']\n",
      "钢铁行业 1\n",
      "['永兴材料']\n",
      "航空机场 1\n",
      "['南方航空']\n",
      "中药 1\n",
      "['红日药业']\n",
      "采掘行业 1\n",
      "['海油工程']\n",
      "保险 1\n",
      "['中国人寿']\n",
      "石油行业 1\n",
      "['广汇能源']\n",
      "工程机械 1\n",
      "['恒立液压']\n",
      "工程建设 1\n",
      "['中国建筑']\n",
      "生物制品 1\n",
      "['智飞生物']\n",
      "化学制药 1\n",
      "['华东医药']\n",
      "医疗器械 1\n",
      "['鱼跃医疗']\n",
      "电子元件 1\n",
      "['振华科技']\n",
      "通信设备 1\n",
      "['亿联网络']\n"
     ]
    }
   ],
   "source": [
    "for i in result_industry:\n",
    "    print(i[0],i[1])\n",
    "    print(result__[i[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(holding_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(holding_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'001667':    序号    股票代码  股票名称  占净值比例     持股数      持仓市值              季度\n",
       " 0   1  600519  贵州茅台   2.73    7.51  12964.59  2022年4季度股票投资明细\n",
       " 1   2  601166  兴业银行   2.52  680.01  11961.32  2022年4季度股票投资明细\n",
       " 2   3  002531  天顺风能   2.20  690.00  10439.70  2022年4季度股票投资明细\n",
       " 3   4  688599  天合光能   2.15  160.00  10201.60  2022年4季度股票投资明细\n",
       " 4   5  000001  平安银行   2.11  760.00  10001.61  2022年4季度股票投资明细\n",
       " 5   6  600176  中国巨石   2.02  700.00   9597.01  2022年4季度股票投资明细\n",
       " 6   7  600660  福耀玻璃   2.00  270.01   9469.18  2022年4季度股票投资明细\n",
       " 7   8  601877  正泰电器   1.93  330.00   9141.00  2022年4季度股票投资明细\n",
       " 8   9  600346  恒力石化   1.83  560.01   8696.90  2022年4季度股票投资明细\n",
       " 9  10  600887  伊利股份   1.83  280.00   8680.00  2022年4季度股票投资明细,\n",
       " '001832':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  000596  古井贡酒   9.42   131.99  35229.17  2022年4季度股票投资明细\n",
       " 1   2  000933  神火股份   7.90  1975.90  29559.46  2022年4季度股票投资明细\n",
       " 2   3  600519  贵州茅台   7.84    16.99  29344.49  2022年4季度股票投资明细\n",
       " 3   4  601899  紫金矿业   7.23  2705.50  27055.03  2022年4季度股票投资明细\n",
       " 4   5  600188  兖矿能源   7.17   798.74  26821.74  2022年4季度股票投资明细\n",
       " 5   6  601699  潞安环能   5.66  1255.95  21162.77  2022年4季度股票投资明细\n",
       " 6   7  600546  山煤国际   5.47  1412.31  20464.44  2022年4季度股票投资明细\n",
       " 7   8  000858   五粮液   5.42   112.25  20283.34  2022年4季度股票投资明细\n",
       " 8   9  600685  中船防务   4.29   773.11  16049.76  2022年4季度股票投资明细\n",
       " 9  10  000983  山西焦煤   3.74  1201.88  14001.86  2022年4季度股票投资明细,\n",
       " '001018':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  002180   纳思达   5.62   801.95  41613.09  2022年4季度股票投资明细\n",
       " 1   2  600519  贵州茅台   5.49    23.55  40675.17  2022年4季度股票投资明细\n",
       " 2   3  300068  南都电源   4.94  1717.92  36591.78  2022年4季度股票投资明细\n",
       " 3   4  000858   五粮液   3.92   160.78  29051.95  2022年4季度股票投资明细\n",
       " 4   5  002594   比亚迪   3.87   111.68  28699.21  2022年4季度股票投资明细\n",
       " 5   6  600845  宝信软件   3.52   582.29  26086.65  2022年4季度股票投资明细\n",
       " 6   7  002049  紫光国微   2.71   152.36  20084.40  2022年4季度股票投资明细\n",
       " 7   8  688066  航天宏图   2.63   228.60  19477.66  2022年4季度股票投资明细\n",
       " 8   9  002756  永兴材料   2.47   198.87  18329.59  2022年4季度股票投资明细\n",
       " 9  10  300750  宁德时代   2.37    44.68  17577.22  2022年4季度股票投资明细,\n",
       " '519002':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  600519  贵州茅台   5.83    24.54  42384.38  2022年4季度股票投资明细\n",
       " 1   2  600027  华电国际   3.38  4178.88  24571.82  2022年4季度股票投资明细\n",
       " 2   3  600754  锦江酒店   3.14   390.43  22781.59  2022年4季度股票投资明细\n",
       " 3   4  601816  京沪高铁   2.97  4381.34  21556.19  2022年4季度股票投资明细\n",
       " 4   5  600559  老白干酒   2.86   755.13  20788.73  2022年4季度股票投资明细\n",
       " 5   6  600029  南方航空   2.47  2360.01  17936.09  2022年4季度股票投资明细\n",
       " 6   7  600690  海尔智家   2.25   669.56  16377.35  2022年4季度股票投资明细\n",
       " 7   8  300026  红日药业   2.21  2828.60  16066.47  2022年4季度股票投资明细\n",
       " 8   9  600583  海油工程   2.17  2603.20  15775.39  2022年4季度股票投资明细\n",
       " 9  10  601628  中国人寿   2.17   424.53  15758.55  2022年4季度股票投资明细,\n",
       " '519198':    序号    股票代码  股票名称  占净值比例     持股数     持仓市值              季度\n",
       " 0   1  601225  陕西煤业   9.10  504.82  9379.62  2022年4季度股票投资明细\n",
       " 1   2  600021  上海电力   8.68  893.48  8943.71  2022年4季度股票投资明细\n",
       " 2   3  601088  中国神华   8.59  320.52  8852.74  2022年4季度股票投资明细\n",
       " 3   4  601898  中煤能源   8.31  993.48  8563.80  2022年4季度股票投资明细\n",
       " 4   5  000776  广发证券   7.34  488.23  7562.68  2022年4季度股票投资明细\n",
       " 5   6  600999  招商证券   6.28  487.15  6479.14  2022年4季度股票投资明细\n",
       " 6   7  600030  中信证券   5.89  305.20  6076.49  2022年4季度股票投资明细\n",
       " 7   8  601001  晋控煤业   5.70  491.09  5873.44  2022年4季度股票投资明细\n",
       " 8   9  600985  淮北矿业   4.94  397.94  5093.63  2022年4季度股票投资明细\n",
       " 9  10  600905  三峡能源   3.95  721.01  4073.71  2022年4季度股票投资明细,\n",
       " '450004':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  600256  广汇能源   3.00  2194.03  19790.16  2022年4季度股票投资明细\n",
       " 1   2  300274  阳光电源   2.96   175.00  19565.23  2022年4季度股票投资明细\n",
       " 2   3  002142  宁波银行   2.89   587.71  19071.19  2022年4季度股票投资明细\n",
       " 3   4  002050  三花智控   2.83   879.03  18652.99  2022年4季度股票投资明细\n",
       " 4   5  002648  卫星化学   2.73  1160.77  17991.94  2022年4季度股票投资明细\n",
       " 5   6  600519  贵州茅台   2.72    10.41  17979.62  2022年4季度股票投资明细\n",
       " 6   7  601100  恒立液压   2.34   244.30  15427.64  2022年4季度股票投资明细\n",
       " 7   8  002475  立讯精密   2.31   479.19  15214.25  2022年4季度股票投资明细\n",
       " 8   9  601658  邮储银行   2.28  3258.53  15054.41  2022年4季度股票投资明细\n",
       " 9  10  600309  万华化学   2.18   155.33  14391.07  2022年4季度股票投资明细,\n",
       " '000991':    序号    股票代码  股票名称  占净值比例      持股数      持仓市值              季度\n",
       " 0   1  601668  中国建筑   9.71  6598.42  35829.41  2022年4季度股票投资明细\n",
       " 1   2  600048  保利发展   8.00  1951.56  29527.10  2022年4季度股票投资明细\n",
       " 2   3  601166  兴业银行   7.85  1647.94  28987.28  2022年4季度股票投资明细\n",
       " 3   4  601009  南京银行   5.74  2034.70  21201.56  2022年4季度股票投资明细\n",
       " 4   5  001979  招商蛇口   5.23  1529.03  19311.65  2022年4季度股票投资明细\n",
       " 5   6  601818  光大银行   4.65  5596.75  17182.02  2022年4季度股票投资明细\n",
       " 6   7  600383  金地集团   4.55  1643.47  16812.70  2022年4季度股票投资明细\n",
       " 7   8  601838  成都银行   4.17  1007.35  15412.51  2022年4季度股票投资明细\n",
       " 8   9  600926  杭州银行   4.10  1155.98  15120.22  2022年4季度股票投资明细\n",
       " 9  10  600919  江苏银行   3.58  1811.77  13207.84  2022年4季度股票投资明细,\n",
       " '001869':    序号    股票代码  股票名称  占净值比例     持股数      持仓市值              季度\n",
       " 0   1  600438  通威股份   7.75  569.56  21973.64  2022年4季度股票投资明细\n",
       " 1   2  300496  中科创达   6.09  172.24  17275.93  2022年4季度股票投资明细\n",
       " 2   3  300122  智飞生物   6.07  196.06  17219.68  2022年4季度股票投资明细\n",
       " 3   4  000963  华东医药   5.38  326.17  15264.85  2022年4季度股票投资明细\n",
       " 4   5  600426  华鲁恒升   5.24  448.60  14871.09  2022年4季度股票投资明细\n",
       " 5   6  002475  立讯精密   4.99  445.72  14151.59  2022年4季度股票投资明细\n",
       " 6   7  002223  鱼跃医疗   4.19  373.41  11896.87  2022年4季度股票投资明细\n",
       " 7   8  601888  中国中免   4.08   53.51  11559.53  2022年4季度股票投资明细\n",
       " 8   9  300450  先导智能   4.07  286.97  11550.59  2022年4季度股票投资明细\n",
       " 9  10  000733  振华科技   3.86   95.86  10950.60  2022年4季度股票投资明细,\n",
       " '110015':    序号    股票代码  股票名称  占净值比例     持股数      持仓市值              季度\n",
       " 0   1  600519  贵州茅台   7.30    8.01  13832.06  2022年4季度股票投资明细\n",
       " 1   2  601012  隆基绿能   7.15  320.87  13559.86  2022年4季度股票投资明细\n",
       " 2   3  600809  山西汾酒   5.84   38.81  11060.35  2022年4季度股票投资明细\n",
       " 3   4  000568  泸州老窖   5.34   45.12  10119.24  2022年4季度股票投资明细\n",
       " 4   5  688639  华恒生物   4.45   54.27   8425.50  2022年4季度股票投资明细\n",
       " 5   6  002049  紫光国微   4.16   59.85   7889.41  2022年4季度股票投资明细\n",
       " 6   7  600233  圆通速递   4.10  386.35   7761.78  2022年4季度股票投资明细\n",
       " 7   8  000858   五粮液   3.75   39.38   7116.35  2022年4季度股票投资明细\n",
       " 8   9  603806   福斯特   3.36   95.85   6368.41  2022年4季度股票投资明细\n",
       " 9  10  300628  亿联网络   3.20  100.22   6072.44  2022年4季度股票投资明细,\n",
       " '001220':    序号    股票代码  股票名称  占净值比例     持股数     持仓市值              季度\n",
       " 0   1  688363  华熙生物   4.89   37.49  5071.17  2022年4季度股票投资明细\n",
       " 1   2  300015  爱尔眼科   4.62  154.23  4791.93  2022年4季度股票投资明细\n",
       " 2   3  300957   贝泰妮   4.58   31.86  4754.79  2022年4季度股票投资明细\n",
       " 3   4  600763  通策医疗   4.54   30.83  4716.68  2022年4季度股票投资明细\n",
       " 4   5  300896   爱美客   4.44    8.14  4610.09  2022年4季度股票投资明细\n",
       " 5   6  600298  安琪酵母   4.30   98.79  4467.28  2022年4季度股票投资明细\n",
       " 6   7  603288  海天味业   3.81   49.74  3959.44  2022年4季度股票投资明细\n",
       " 7   8  603345  安井食品   3.81   24.42  3953.11  2022年4季度股票投资明细\n",
       " 8   9  600887  伊利股份   3.57  119.44  3702.64  2022年4季度股票投资明细\n",
       " 9  10  603605   珀莱雅   3.38   20.94  3506.53  2022年4季度股票投资明细}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holding_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "001667\n",
      "001832\n",
      "001018\n",
      "519002\n",
      "519198\n",
      "450004\n",
      "000991\n",
      "001869\n",
      "110015\n",
      "001220\n"
     ]
    }
   ],
   "source": [
    "ratio={}\n",
    "for k,v in holding_dict.items():\n",
    "    print(k)\n",
    "    ratio[fund_dict[k]]=round(v.iloc[:10]['占净值比例'].sum(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "南方转型混合A(F001667) 21.32\n",
      "易方达瑞恒混合(F001832) 64.14\n",
      "易方达新经济混合(F001018) 37.54\n",
      "华安安信消费混合(F519002) 29.45\n",
      "万家颐和灵活配置混合(F519198) 68.78\n",
      "国富深化价值混合(F450004) 26.24\n",
      "工银战略转型股票(F000991) 57.58\n",
      "招商制造业混合(F001869) 51.72\n",
      "易方达行业领先企业(F110015) 48.65\n",
      "民生加银研究精选混合(F001220) 41.94\n"
     ]
    }
   ],
   "source": [
    "for k ,v in ratio.items():\n",
    "    print(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ratio_ = sorted(ratio.items(),key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['基金代码', '基金简称', '单位净值', '总募集规模', '最近总份额', '成立日期', '基金经理', '更新日期'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb Cell 119'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb#ch0000107?line=0'>1</a>\u001b[0m fund_scale_open_sina_df \u001b[39m=\u001b[39m ak\u001b[39m.\u001b[39;49mfund_scale_open_sina(symbol\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m股票型基金\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/xda/github/stock/fund/qdii_fund_analysis_5fund.ipynb#ch0000107?line=1'>2</a>\u001b[0m \u001b[39mprint\u001b[39m(fund_scale_open_sina_df)\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py:70\u001b[0m, in \u001b[0;36mfund_scale_open_sina\u001b[0;34m(symbol)\u001b[0m\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=44'>45</a>\u001b[0m temp_df[\u001b[39m\"\u001b[39m\u001b[39mindex\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m, \u001b[39mlen\u001b[39m(temp_df) \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m)\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=45'>46</a>\u001b[0m temp_df\u001b[39m.\u001b[39mrename(\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=46'>47</a>\u001b[0m     columns\u001b[39m=\u001b[39m{\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=47'>48</a>\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mindex\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m序号\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=67'>68</a>\u001b[0m     inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=68'>69</a>\u001b[0m )\n\u001b[0;32m---> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=69'>70</a>\u001b[0m temp_df \u001b[39m=\u001b[39m temp_df[\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=70'>71</a>\u001b[0m     [\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=71'>72</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m序号\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=72'>73</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m基金代码\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=73'>74</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m基金简称\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=74'>75</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m单位净值\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=75'>76</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m总募集规模\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=76'>77</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m最近总份额\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=77'>78</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m成立日期\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=78'>79</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m基金经理\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=79'>80</a>\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m更新日期\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=80'>81</a>\u001b[0m     ]\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=81'>82</a>\u001b[0m ]\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=82'>83</a>\u001b[0m temp_df[\u001b[39m\"\u001b[39m\u001b[39m成立日期\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(temp_df[\u001b[39m\"\u001b[39m\u001b[39m成立日期\u001b[39m\u001b[39m\"\u001b[39m])\u001b[39m.\u001b[39mdt\u001b[39m.\u001b[39mdate\n\u001b[1;32m     <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/akshare/fund/fund_scale_sina.py?line=83'>84</a>\u001b[0m temp_df[\u001b[39m\"\u001b[39m\u001b[39m更新日期\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(temp_df[\u001b[39m\"\u001b[39m\u001b[39m更新日期\u001b[39m\u001b[39m\"\u001b[39m])\u001b[39m.\u001b[39mdt\u001b[39m.\u001b[39mdate\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py:3511\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3508'>3509</a>\u001b[0m     \u001b[39mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3509'>3510</a>\u001b[0m         key \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(key)\n\u001b[0;32m-> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3510'>3511</a>\u001b[0m     indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49m_get_indexer_strict(key, \u001b[39m\"\u001b[39;49m\u001b[39mcolumns\u001b[39;49m\u001b[39m\"\u001b[39;49m)[\u001b[39m1\u001b[39m]\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3512'>3513</a>\u001b[0m \u001b[39m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/frame.py?line=3513'>3514</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(indexer, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m) \u001b[39m==\u001b[39m \u001b[39mbool\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py:5782\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5778'>5779</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5779'>5780</a>\u001b[0m     keyarr, indexer, new_indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5781'>5782</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5783'>5784</a>\u001b[0m keyarr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtake(indexer)\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5784'>5785</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, Index):\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5785'>5786</a>\u001b[0m     \u001b[39m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py:5845\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5841'>5842</a>\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNone of [\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m] are in the [\u001b[39m\u001b[39m{\u001b[39;00maxis_name\u001b[39m}\u001b[39;00m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5843'>5844</a>\u001b[0m not_found \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[39m.\u001b[39mnonzero()[\u001b[39m0\u001b[39m]]\u001b[39m.\u001b[39munique())\n\u001b[0;32m-> <a href='file:///~/miniconda3/envs/cpy/lib/python3.9/site-packages/pandas/core/indexes/base.py?line=5844'>5845</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mnot_found\u001b[39m}\u001b[39;00m\u001b[39m not in index\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['基金代码', '基金简称', '单位净值', '总募集规模', '最近总份额', '成立日期', '基金经理', '更新日期'] not in index\""
     ]
    }
   ],
   "source": [
    "# 无法使用\n",
    "fund_scale_open_sina_df = ak.fund_scale_open_sina(symbol='股票型基金')\n",
    "print(fund_scale_open_sina_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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